{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T01:26:09Z","timestamp":1772846769908,"version":"3.50.1"},"reference-count":109,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,5,6]],"date-time":"2021-05-06T00:00:00Z","timestamp":1620259200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61521002"],"award-info":[{"award-number":["61521002"]}]},{"name":"National Natural Science Foundation of China","award":["61872214"],"award-info":[{"award-number":["61872214"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>E-Bibliotherapy deals with adolescent psychological stress by manually or automatically recommending multiple reading articles around their stressful events, using electronic devices as a medium. To make E-Bibliotherapy really useful, generating instructive questions before their reading is an important step. Such a question shall (a) attract teens\u2019 attention; (b) convey the essential message of the reading materials so as to improve teens\u2019 active comprehension; and most importantly (c) highlight teens\u2019 stress to enable them to generate emotional resonance and thus willingness to pursue the reading. Therefore in this paper, we propose to generate instructive questions from the multiple recommended articles to guide teens to read. Four solutions based on the neural encoder-decoder model are presented to tackle the task. For model training and testing, we construct a novel large-scale QA dataset named TeenQA, which is specific to adolescent stress. Due to the extensibility of question expressions, we incorporate three groups of automatic evaluation metrics as well as one group of human evaluation metrics to examine the quality of the generated questions. The experimental results show that the proposed Encoder-Decoder with Summary on Contexts with Feature-rich embeddings (ED-SoCF) solution can generate good questions for guiding reading, achieving comparable performance on some semantic similarity metrics with that of humans.<\/jats:p>","DOI":"10.3390\/s21093223","type":"journal-article","created":{"date-parts":[[2021,5,6]],"date-time":"2021-05-06T11:10:27Z","timestamp":1620299427000},"page":"3223","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Generating Instructive Questions from Multiple Articles to Guide Reading in E-Bibliotherapy"],"prefix":"10.3390","volume":"21","author":[{"given":"Yunxing","family":"Xin","sequence":"first","affiliation":[{"name":"Centre for Computational Mental Healthcare, Department of Computer Science and Technology, Research Institute of Data Science, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2778-6870","authenticated-orcid":false,"given":"Lei","family":"Cao","sequence":"additional","affiliation":[{"name":"Centre for Computational Mental Healthcare, Department of Computer Science and Technology, Research Institute of Data Science, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"Centre for Computational Mental Healthcare, Department of Computer Science and Technology, Research Institute of Data Science, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohao","family":"He","sequence":"additional","affiliation":[{"name":"Centre for Computational Mental Healthcare, Department of Computer Science and Technology, Research Institute of Data Science, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ling","family":"Feng","sequence":"additional","affiliation":[{"name":"Centre for Computational Mental Healthcare, Department of Computer Science and Technology, Research Institute of Data Science, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,6]]},"reference":[{"key":"ref_1","unstructured":"Burns, D.D. (1980). Feeling Good: The New Mood Therapy, William Morrow and Company."},{"key":"ref_2","unstructured":"Crothers, S.M. (1917). A Literary Clinic, Houghton Mifflin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1002\/cpp.679","article-title":"Patients\u2019 and providers\u2019 perspectives on bibliotherapy in primary care","volume":"17","author":"McKenna","year":"2010","journal-title":"Clin. Psychol. Psychother."},{"key":"ref_4","unstructured":"Rahmania, A.M., Lai, J., Jafarloua, S., Yunusova, A., Rivera, A., Labbaf, S., Hu, S., Anzanpour, A., Dutt, N., and Jain, R. (2020). Personal Mental Health Navigator: Harnessing the Power of Data, Personal Models, and Health Cybernetics to Promote Psychological Well-being. arXiv."},{"key":"ref_5","first-page":"90","article-title":"E-Bibliotherapy, Computer Based Bibliotherapy\u2014Development Perspectives in Relation to The Effectiveness, Reliability and Economy","volume":"5","author":"Elizabeth","year":"2016","journal-title":"Adv. Sci. Eng. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Xin, Y., Chen, Y., Jin, L., Cai, Y., and Feng, L. (2017, January 25\u201330). TeenRead: An Adolescents Reading Recommendation System Towards Online Bibliotherapy. Proceedings of the 2017 IEEE International Congress on Big Data (BigData Congress), Honolulu, HI, USA.","DOI":"10.1109\/BigDataCongress.2017.63"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"166","DOI":"10.2307\/747482","article-title":"Active Comprehension: Problem-Solving Schema with Question Generation for Comprehension of Complex Short Stories","volume":"17","author":"Singer","year":"1982","journal-title":"Read. Res. Q."},{"key":"ref_8","unstructured":"Heilman, M. (2011). Automatic Factual Question Generation from Text. [Ph.D. Thesis, Carnegie Mellon University]. AAI3528179."},{"key":"ref_9","unstructured":"Heilman, M., and Smith, N.A. (2010, January 2\u20134). Good question! statistical ranking for question generation. Proceedings of the 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Los Angeles, CA, USA."},{"key":"ref_10","unstructured":"Du, X., Shao, J., and Cardie, C. (August, January 30). Learning to ask: Neural question generation for reading comprehension. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, BC, Canada."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hase, P., and Bansal, M. (2020, January 5\u201310). Evaluating explainable AI: Which algorithmic explanations help users predict model behavior?. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online.","DOI":"10.18653\/v1\/2020.acl-main.491"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Duan, N., Tang, D., Chen, P., and Zhou, M. (2017, January 9\u201311). Question Generation for Question Answering. Proceedings of the International Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark.","DOI":"10.18653\/v1\/D17-1090"},{"key":"ref_13","unstructured":"Tang, D., Duan, N., Qin, T., and Zhou, M. (2017). Question Answering and Question Generation as Dual Tasks. arXiv."},{"key":"ref_14","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2019, January 2\u20137). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, MN, USA."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kamath, A., Jia, R., and Liang, P. (2020, January 5\u201310). Selective question answering under domain shift. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online.","DOI":"10.18653\/v1\/2020.acl-main.503"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Serban, I., Garc\u00eda-Dur\u00e1n, A., Gulcehre, C., Ahn, S., Chandar, S., Courville, A., and Bengio, Y. (2016, January 7\u201312). Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany.","DOI":"10.18653\/v1\/P16-1056"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"75","DOI":"10.5087\/dad.2012.204","article-title":"Question generation from concept maps","volume":"3","author":"Olney","year":"2012","journal-title":"Dialogue Discourse"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"11","DOI":"10.5087\/dad.2012.202","article-title":"Semantics-based question generation and implementation","volume":"3","author":"Yao","year":"2012","journal-title":"Dialogue Discourse"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1162\/COLI_a_00206","article-title":"Towards topic-to-question generation","volume":"41","author":"Chali","year":"2015","journal-title":"Comput. Linguist."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Labutov, I., Basu, S., and Vanderwende, L. (2015, January 26\u201331). Deep Questions without Deep Understanding. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Beijing, China.","DOI":"10.3115\/v1\/P15-1086"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhou, Q., Yang, N., Wei, F., Tan, C., Bao, H., and Zhou, M. (2017, January 8\u201312). Neural Question Generation from Text: A Preliminary Study. Proceedings of the Natural Language Processing and Chinese Computing, Dalian, China.","DOI":"10.1007\/978-3-319-73618-1_56"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yuan, X., Wang, T., Gulcehre, C., Sordoni, A., Bachman, P., Subramanian, S., Zhang, S., and Trischler, A. (2017, January 3). Machine Comprehension by Text-to-Text Neural Question Generation. Proceedings of the 2nd Workshop on Representation Learning for NLP, Vancouver, BC, Canada.","DOI":"10.18653\/v1\/W17-2603"},{"key":"ref_23","unstructured":"Lindberg, D., Popowich, F., Nesbit, J., and Winne, P. (2013, January 8\u20139). Generating natural language questions to support learning on-line. Proceedings of the 14th European Workshop on Natural Language Generation, Sofia, Bulgaria."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chali, Y., and Golestanirad, S. (2016, January 5\u20138). Ranking automatically generated questions using common human queries. Proceedings of the 9th International Natural Language Generation Conference, Edinburgh, UK.","DOI":"10.18653\/v1\/W16-6635"},{"key":"ref_25","unstructured":"Sutskever, I., Vinyals, O., and Le, Q.V. (2014). Sequence to sequence learning with neural networks. arXiv."},{"key":"ref_26","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv."},{"key":"ref_27","unstructured":"Lopyrev, K. (2015). Generating news headlines with recurrent neural networks. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Nallapati, R., Zhou, B., dos Santos, C., Gul\u00e7ehre, \u00c7., and Xiang, B. (2016, January 11\u201312). Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond. Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning (CoNLL), Berlin, Germany.","DOI":"10.18653\/v1\/K16-1028"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bahdanau, D., Chorowski, J., Serdyuk, D., Brakel, P., and Bengio, Y. (2016, January 20\u201325). End-to-end attention-based large vocabulary speech recognition. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China.","DOI":"10.1109\/ICASSP.2016.7472618"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Venugopalan, S., Rohrbach, M., Donahue, J., Mooney, R., Darrell, T., and Saenko, K. (2015, January 7\u201313). Sequence to sequence-video to text. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.515"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P. (2016, January 1\u20134). Squad: 100,000+ questions for machine comprehension of text. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, TX, USA.","DOI":"10.18653\/v1\/D16-1264"},{"key":"ref_32","unstructured":"Nguyen, T., Rosenberg, M., Song, X., Gao, J., Tiwary, S., Majumder, R., and Deng, L. (2016, January 9). MS MARCO: A human generated machine reading comprehension dataset. Proceedings of the International Workshop on Advances in Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_33","unstructured":"Joshi, M., Choi, E., Weld, D., and Zettlemoyer, L. (August, January 30). TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, BC, Canada."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Trischler, A., Wang, T., Yuan, X., Harris, J., Sordoni, A., Bachman, P., and Suleman, K. (2016). Newsqa: A machine comprehension dataset. arXiv.","DOI":"10.18653\/v1\/W17-2623"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yang, Y., Yih, W.-T., and Meek, C. (2015, January 17\u201321). Wikiqa: A challenge dataset for open-domain question answering. Proceedings of the International Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal.","DOI":"10.18653\/v1\/D15-1237"},{"key":"ref_36","unstructured":"Bordes, A., Usunier, N., Chopra, S., and Weston, J. (2015). Large-scale simple question answering with memory networks. arXiv."},{"key":"ref_37","unstructured":"Dorr, B., Zajic, D., and Schwartz, R. (June, January 27). Hedge trimmer: A parse-and-trim approach to headline generation. Proceedings of the HLT-NAACL 03 on Text Summarization Workshop, Edmonton, AB, Canada."},{"key":"ref_38","unstructured":"Gattani, A. (2007). Automated Natural Language Headline Generation Using Discriminative Machine Learning Models. [Master\u2019s Thesis, Simon Fraser University]."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Banko, M., Mittal, V.O., and Witbrock, M.J. (2000, January 1\u20138). Headline generation based on statistical translation. Proceedings of the the Annual Meeting on Association for Computational Linguistics, Hong Kong, China.","DOI":"10.3115\/1075218.1075259"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Jin, R., and Hauptmann, A.G. (2001, January 18\u201321). Automatic title generation for spoken broadcast news. Proceedings of the First International Conference on Human Language Technology Research, San Diego, CA, USA.","DOI":"10.3115\/1072133.1072144"},{"key":"ref_41","unstructured":"Zajic, D., Dorr, B., and Schwartz, R. (2002, January 6\u201312). Automatic Headline Generation for Newspaper Stories. Proceedings of the ACL Workshop on DUC, Philadelphia, PA, USA."},{"key":"ref_42","first-page":"703","article-title":"Statistics-based summarization-step one: Sentence compression","volume":"2000","author":"Knight","year":"2000","journal-title":"AAAI\/IAAI"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Unno, Y., Ninomiya, T., Miyao, Y., and Tsujii, J. (2006, January 17\u201321). Trimming CFG parse trees for sentence compression using machine learning approaches. Proceedings of the COLING\/ACL on Main Conference Poster Sessions, Sydney, Australia.","DOI":"10.3115\/1273073.1273182"},{"key":"ref_44","unstructured":"Reimer, U., and Hahn, U. (1988, January 14\u201318). Text condensation as knowledge base abstraction. Proceedings of the Fourth International Conference on Artificial Intelligence Applications, San Diego, CA, USA."},{"key":"ref_45","unstructured":"Skorokhodko, E. (1971, January 23\u201328). Adaptive method of automatic abstracting and indexing. Proceedings of the IFIP Congress, Ljubljana, Yugoslavia."},{"key":"ref_46","unstructured":"Marcu, D. (1997, January 7\u201312). From discourse structures to text summaries. Proceedings of the ACL Workshop on Intelligent Scalable Text Summarization, Madrid, Spain."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Goldstein, J., Kantrowitz, M., Mittal, V., and Carbonell, J. (1999, January 15\u201319). Summarizing text documents: Sentence selection and evaluation metrics. Proceedings of the ACM SIGIR, Berkeley, CA, USA.","DOI":"10.1145\/312624.312665"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/S0306-4573(96)00062-3","article-title":"Automatic text structuring and summary","volume":"33","author":"Salton","year":"1997","journal-title":"Inf. Process. Manag."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1147\/rd.22.0159","article-title":"The automatic creation of literature abstracts","volume":"2","author":"Luhn","year":"1958","journal-title":"IBM J. Res. Dev."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1613\/jair.1523","article-title":"LexRank: Graph-based Lexical Centrality as Salience in Text Summarization","volume":"22","author":"Erkan","year":"2004","journal-title":"J. Artif. Intell. Res."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1145\/321510.321519","article-title":"New methods in automatic extracting","volume":"16","author":"Edmundson","year":"1969","journal-title":"J. Assoc. Comput. Mach."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/0306-4573(90)90014-S","article-title":"Constructing literature abstracts by computer: Techniques and prospects","volume":"26","author":"Paice","year":"1990","journal-title":"Inf. Process. Manag."},{"key":"ref_53","unstructured":"Paice, C., and Jones, P. (July, January 27). The identification of important concepts in highly structured technical papers. Proceedings of the ACM SIGIR, Pittsburgh, PA, USA."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1023\/A:1009976227802","article-title":"Learning algorithms for keyphrase extraction","volume":"2","author":"Turney","year":"2000","journal-title":"Inf. Retr."},{"key":"ref_55","unstructured":"Frank, E., Paynter, G., Witten, I., Gutwin, C., and Nevill-Manning, C. (August, January 31). Domain-specific keyphrase extraction. Proceedings of the IJCAI, Stockholm, Sweden."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/S0004-3702(02)00222-9","article-title":"Summarization beyond sentence extraction: A probabilistic approach to sentence compression","volume":"139","author":"Knight","year":"2002","journal-title":"Artif. Intell."},{"key":"ref_57","unstructured":"Zajic, D., Dorr, B., and Schwartz, R. (2004, January 2\u20137). Bbn\/umd at DUC-2004: Topiary. Proceedings of the HLT-NAACL 2004 Document Understanding Workshop, Boston, MA, USA."},{"key":"ref_58","unstructured":"Colmenares, C.A., Litvak, M., Mantrach, A., and Silvestri, F. (June, January 31). HEADS: Headline Generation as Sequence Prediction Using an Abstract Feature-Rich Space. Proceedings of the HLT-NAACL, Denver, CO, USA."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Filippova, K., Alfonseca, E., Colmenares, C.A., Kaiser, L., and Vinyals, O. (2015, January 17\u201321). Sentence Compression by Deletion with LSTMs. Proceedings of the EMNLP, Lisbon, Portugal.","DOI":"10.18653\/v1\/D15-1042"},{"key":"ref_60","unstructured":"Filippova, K. (2010, January 23\u201327). Multi-sentence compression: Finding shortest paths in word graphs. Proceedings of the 23rd International Conference on Computational Linguistics, Beijing, China."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Tan, J., Wan, X., and Xiao, J. (2017, January 19\u201325). From Neural Sentence Summarization to Headline Generation: A Coarse-to-Fine Approach. Proceedings of the Twenty-Sixth Intl. Joint Conf. on Artificial Intelligence IJCAI, Melbourne, Australia.","DOI":"10.24963\/ijcai.2017\/574"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Tseng, Y.H., Lin, C.J., Chen, H.H., and Lin, Y.I. (2006). Toward generic title generation for clustered documents. Inf. Retr. Technol., 145\u2013157.","DOI":"10.1007\/11880592_12"},{"key":"ref_63","unstructured":"Xu, S., Yang, S., and Lau, F.C.M. Keyword Extraction and Headline Generation Using Novel Word Features. Proceedings of the AAAI."},{"key":"ref_64","unstructured":"Genest, P.E., and Lapalme, G. (2012, January 8\u201314). Fully abstractive approach to guided summarization. Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers-Volume 2, Jeju Island, Korea."},{"key":"ref_65","unstructured":"Alfonseca, E., Pighin, D., and Garrido, G. (2013, January 4\u20139). HEADY: News headline abstraction through event pattern clustering. Proceedings of the ACL, Sofia, Bulgaria."},{"key":"ref_66","unstructured":"Sun, R., Zhang, Y., Zhang, M., and Ji, D.H. (2015, January 26\u201331). Event-Driven Headline Generation. Proceedings of the ACL, Beijing, China."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Luong, M., Pham, H., and Manning, C. (2015, January 17\u201321). Effective approaches to attention-based neural machine translation. Proceedings of the International Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal.","DOI":"10.18653\/v1\/D15-1166"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Sennrich, R., Haddow, B., and Birch, A. (2016, January 7\u201312). Neural Machine Translation of Rare Words with Subword Units. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany.","DOI":"10.18653\/v1\/P16-1162"},{"key":"ref_69","unstructured":"Wu, Y., Schuster, M., Chen, Z., Le, V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., and Macherey, K. (2016). Google\u2019s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. arXiv."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Rush, A.M., Chopra, S., and Weston, J. (2015, January 17\u201321). A Neural Attention Model for Sentence Summarization. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal.","DOI":"10.18653\/v1\/D15-1044"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Chopra, S., Auli, M., and Rush, A.M. (2016, January 12\u201317). Abstractive sentence summarization with attentive recurrent neural networks. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, CA, USA.","DOI":"10.18653\/v1\/N16-1012"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Gu, J., Lu, Z., Li, H., and Li, V.O. (2016, January 7\u201312). Incorporating copying mechanism in sequence-to-sequence learning. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany.","DOI":"10.18653\/v1\/P16-1154"},{"key":"ref_73","unstructured":"Kalchbrenner, N., and Blunsom, P. (2013, January 18\u201321). Recurrent Continuous Translation Models. Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP), Grand Hyatt, Seattle, WA, USA."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Tai, K.S., Socher, R., and Manning, C.D. (2015). Improved semantic representations from tree-structured long short-term memory networks. arXiv.","DOI":"10.3115\/v1\/P15-1150"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Takase, S., Suzuki, J., Okazaki, N., Hirao, T., and Nagata, M. (2016, January 1\u20134). Neural Headline Generation on Abstract Meaning Representation. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP), Austin, TX, USA.","DOI":"10.18653\/v1\/D16-1112"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Li, J., Luong, M.T., and Jurafsky, D. (2015, January 26\u201331). A Hierarchical Neural Autoencoder for Paragraphs and Documents. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL), Beijing, China.","DOI":"10.3115\/v1\/P15-1107"},{"key":"ref_77","unstructured":"Ali, H., Chali, Y., and Hasan, S.A. (2010, January 14\u201318). Automation of question generation from sentences. Proceedings of the Third Workshop on Question Generation (QG), Pittsburgh, PA, USA."},{"key":"ref_78","unstructured":"Kalady, S., Elikkottil, A., and Das, R. (2010, January 14\u201318). Natural language question generation using syntax and keywords. Proceedings of the Third Workshop on Question Generation (QG), Pittsburgh, PA, USA."},{"key":"ref_79","unstructured":"Mitkov, R., and Ha, L. (June, January 27). Computer-aided generation of multiple-choice tests. Proceedings of the HLT-NAACL 03 Workshop on Building Educational Applications Using Natural Language Processing, Edmonton, AB, Canada."},{"key":"ref_80","unstructured":"Rus, V., Wyse, B., Piwek, P., Lintean, M., Stoyanchev, S., and Moldovan, C. (2010, January 7\u20139). The first question generation shared task evaluation challenge. Proceedings of the 6th International Conference on Natural Language Generation, Trim, Co. Meath, Ireland."},{"key":"ref_81","unstructured":"Mostow, J., and Chen, W. (2009, January 6\u201310). Generating instruction automatically for the reading strategy of self-questioning. Proceedings of the 2nd Workshop on Question Generation (AIED), Brighton, UK."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Mazidi, K., and Nielsen, R. (2014, January 22\u201327). Linguistic considerations in automatic question generation. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Baltimore, MD, USA.","DOI":"10.3115\/v1\/P14-2053"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Bollacker, K., Evans, C., Paritosh, P., Sturge, T., and Taylor, J. (2008, January 10\u201312). Freebase: A collaboratively created graph database for structuring human knowledge. Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, Vancouver, BC, Canada.","DOI":"10.1145\/1376616.1376746"},{"key":"ref_84","unstructured":"Richardson, M., Burges, C.J., and Renshaw, E. (2013, January 18\u201321). MCTest: A challenge dataset for the open-domain machine comprehension of text. Proceedings of the International Conference on Empirical Methods in Natural Language Processing (EMNLP), Grand Hyatt, Seattle, WA, USA."},{"key":"ref_85","unstructured":"Hermann, K.M., Ko\u010dcisk\u00fd, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., and Blunsom, P. (2015, January 7\u201312). Teaching Machines to Read and Comprehend. Proceedings of the International Conference on Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_86","first-page":"345","article-title":"A survey on question answering systems with classification","volume":"28","author":"Mishra","year":"2016","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1007\/s11390-017-1758-3","article-title":"Recent Advances on Neural Headline Generation","volume":"32","author":"Shen","year":"2017","journal-title":"J. Comput. Sci. Technol."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merrienboer, B., Bahdanau, D., and Bengio, Y. (2014, January 25). On the properties of neural machine translation: Encoder-decoder approaches. Proceedings of the Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation (SSST-8), Doha, Qatar.","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1016\/j.ipm.2003.10.006","article-title":"Centroid-based summarization of multiple documents","volume":"40","author":"Radev","year":"2004","journal-title":"Inf. Process. Manag."},{"key":"ref_92","unstructured":"Mihalcea, R., and Tarau, P. (2004, January 25\u201326). Textrank: Bringing order into text. Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, Barcelona, Spain."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Gillick, D., and Favre, B. (2009, January 4). A scalable global model for summarization. Proceedings of the Workshop on Integer Linear Programming for Natural Langauge Processing, Boulder, CO, USA.","DOI":"10.3115\/1611638.1611640"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Wan, X., and Yang, J. (2008, January 20\u201324). Multi-document summarization using cluster-based link analysis. Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval ACM, Singapore.","DOI":"10.1145\/1390334.1390386"},{"key":"ref_95","unstructured":"Lin, H., and Bilmes, J. (2010, January 2\u20134). Multi-document summarization via budgeted maximization of submodular functions. Proceedings of the Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, Los Angeles, CA, USA."},{"key":"ref_96","unstructured":"Zhang, J., Wang, T., and Wan, X. (2016, January 11\u201316). PKUSUMSUM: A Java Platform for Multilingual Document Summarization. Proceedings of the COLING (Demos), Osaka, Japan."},{"key":"ref_97","unstructured":"Kingma, D., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Papineni, K., Roukos, S., Ward, T., and Zhu, W.J. (2002, January 6\u201312). BLEU: A method for automatic evaluation of machine translation. Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, Philadelphia, PA, USA.","DOI":"10.3115\/1073083.1073135"},{"key":"ref_99","unstructured":"Lin, C.Y. (2004, January 21\u201326). Rouge: A package for automatic evaluation of summaries. Proceedings of the Text Summarization Branches Out: Proceedings of the ACL-04 Workshop, Barcelona, Spain."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Denkowski, M., and Lavie, A. (2014, January 26\u201327). Meteor universal: Language specific translation evaluation for any target language. Proceedings of the Ninth Workshop on Statistical Machine Translation, Baltimore, MD, USA.","DOI":"10.3115\/v1\/W14-3348"},{"key":"ref_101","unstructured":"Sharma, S., Asri, L.E., Schulz, H., and Zumer, J. (2017). Relevance of unsupervised metrics in task-oriented dialogue for evaluating natural language generation. arXiv."},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Vedantam, R., Lawrence Zitnick, C., and Parikh, D. (2015, January 7\u201312). Cider: Consensus-based image description evaluation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299087"},{"key":"ref_103","unstructured":"Kiros, R., Zhu, Y., Salakhutdinov, R.R., Zemel, R., Urtasun, R., Torralba, A., and Fidler, S. (2015, January 7\u201312). Skip-thought vectors. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_104","unstructured":"Forgues, G., Pineau, J., Larchev\u00eaque, J.M., and Tremblay, R. (2014, January 12\u201313). Bootstrapping dialog systems with word embeddings. Proceedings of the International Workshop on Modern Machine Learning and Natural Language Processing, Montreal, QC, Canada."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Rus, V., and Lintean, M. (2012, January 7). A comparison of greedy and optimal assessment of natural language student input using word-to-word similarity metrics. Proceedings of the Seventh Workshop on Building Educational Applications Using NLP, Montreal, QC, Canada.","DOI":"10.1007\/978-3-642-30950-2_116"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Robertson, S., and Walker, S. (1994, January 3\u20136). Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. Proceedings of the SIGIR, Dublin, Ireland.","DOI":"10.1007\/978-1-4471-2099-5_24"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Luong, M.T., Pham, H., and Manning, C.D. (2015). Effective approaches to attention-based neural machine translation. arXiv.","DOI":"10.18653\/v1\/D15-1166"},{"key":"ref_108","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the NIPS, Long Beach, CA, USA."},{"key":"ref_109","unstructured":"Zihang, D., Zhilin, Y., Yiming, Y., Carbonell, J., Le, Q.V., and Salakhutdinov, R. (August, January 28). Transformer-XL: Attentive language models beyond a fixed-length context. Proceedings of the ACL, Florence, Italy."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/9\/3223\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:57:38Z","timestamp":1760162258000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/9\/3223"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,6]]},"references-count":109,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["s21093223"],"URL":"https:\/\/doi.org\/10.3390\/s21093223","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,6]]}}}