{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T00:25:49Z","timestamp":1770078349722,"version":"3.49.0"},"reference-count":94,"publisher":"Cambridge University Press (CUP)","issue":"1","license":[{"start":{"date-parts":[[2018,10,31]],"date-time":"2018-10-31T00:00:00Z","timestamp":1540944000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Nat. Lang. Eng."],"published-print":{"date-parts":[[2019,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>By increasing the amount of data in computer networks, searching and finding suitable information will be harder for users. One of the most widespread forms of information on such networks are textual documents. So exploring these documents to get information about their content is difficult and sometimes impossible. Multi-document text summarization systems are an aid to producing a summary with a fixed and predefined length, while covering the maximum content of the input documents. This paper presents a novel method for multi-document extractive summarization based on textual entailment relations and sentence compression via formulating the problem as a knapsack problem. In this approach, sentences of documents are ranked according to the extended Tf-Idf method, then entailment scores of selected sentences are computed. Through these scores, the final score of each sentence is calculated. Finally, by decreasing the lengths of sentences via sentence compression, the problem has been solved by greedy and dynamic Programming approaches to the knapsack problem. Experiments on standard summarization datasets and evaluating the results based on the Rouge system show that the suggested method, according to the best of our knowledge, has increased F-measure of query-based summarization systems by two per cent and F-measure of general summarization systems by five per cent.<\/jats:p>","DOI":"10.1017\/s1351324918000414","type":"journal-article","created":{"date-parts":[[2018,10,31]],"date-time":"2018-10-31T10:49:27Z","timestamp":1540982967000},"page":"121-146","source":"Crossref","is-referenced-by-count":7,"title":["Extractive multi-document summarization based on textual entailment and sentence compression via knapsack problem"],"prefix":"10.1017","volume":"25","author":[{"given":"ALI","family":"NASERASADI","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"HAMID","family":"KHOSRAVI","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"FARAMARZ","family":"SADEGHI","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2018,10,31]]},"reference":[{"key":"S1351324918000414_ref016","doi-asserted-by":"crossref","unstructured":"Chuang W. , and Yang J. 2000. Extracting sentence segments for text summarization: a machine learning approach, In Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, pp. 152\u20139.","DOI":"10.1145\/345508.345566"},{"key":"S1351324918000414_ref091","unstructured":"Wang L. , Raghavan H. , Castelli V. , Florian R. , and Cardie C. 2016. A sentence compression based framework to query-focused multi-document summarization. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). ACL. pp. 1384\u20131394."},{"key":"S1351324918000414_ref079","unstructured":"Rankel P. , Conroy J. , Dang H. , and Nenkova A. 2013. A decade of automatic content evaluation of news summaries: reassessing the state of the art. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL-2013), vol. 2, pp. 131\u20136."},{"key":"S1351324918000414_ref066","unstructured":"Nenkova A. 2006. Understanding the Process of Multi-Document Summarization: Content Selection, Rewriting and Evaluation. PhD dissertation, Columbia University."},{"key":"S1351324918000414_ref060","doi-asserted-by":"crossref","unstructured":"Magnini B. , Zanoli R. , Dagan I. , Eichler K. , Neumann G. , Noh T. , Pado S. , Stern A. , and Levy O. 2014. The excitement open platform for textual inferences. In Proceedings of the Association for Computational Linguistics (System Demonstrations), pp. 43\u20138.","DOI":"10.3115\/v1\/P14-5008"},{"key":"S1351324918000414_ref058","doi-asserted-by":"crossref","unstructured":"Louis A. , and Nenkova A. 2009. Automatically evaluating content selection in summarization without human models. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (EMNLP-2009), pp. 306\u201314.","DOI":"10.3115\/1699510.1699550"},{"key":"S1351324918000414_ref047","unstructured":"Li S. , Ouyang Y. , Wang W. , and Sun B. 2007. Multi-document summarization using support vector regression. In Proceedings of Document Understanding Conference (DUC-2007)."},{"key":"S1351324918000414_ref080","unstructured":"Riedhammer K. , Gillick D. , Favre B. , and Hakkani-Tur D. 2008. Packing the meeting summarization knapsack. In Proceedings of the INTERSPEECH, pp. 2434\u20137."},{"key":"S1351324918000414_ref053","unstructured":"Litvak M. , Last M. , and Friedman M. 2010. A new approach to improving multilingual summarization using a genetic algorithm. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, ACL, pp. 927\u201336."},{"key":"S1351324918000414_ref025","doi-asserted-by":"publisher","DOI":"10.1145\/321510.321519"},{"key":"S1351324918000414_ref090","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2007.01.023"},{"key":"S1351324918000414_ref088","first-page":"26","article-title":"Summarization by logic segmentation and text entailment","volume":"15","author":"Tatar","year":"2008","journal-title":"Advances in Natural Language Processing and Applications"},{"key":"S1351324918000414_ref040","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401941"},{"key":"S1351324918000414_ref061","volume-title":"Advances in automatic text summarization","author":"Mani","year":"1999"},{"key":"S1351324918000414_ref007","unstructured":"Berg-Kirkpatrick T. , Gillick D. , and Klein D. 2011. Jointly learning to extract and compress. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 481\u201390."},{"key":"S1351324918000414_ref017","doi-asserted-by":"crossref","unstructured":"Conroy J. , and O\u2019leary D. 2001. Text summarization via hidden markov models. In Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, pp. 406\u20137.","DOI":"10.1145\/383952.384042"},{"key":"S1351324918000414_ref035","unstructured":"He Z. , Chen C. , Bu J. , Wang C. , Zhang L. , Cai D. , and He X. 2012. Document summarization based on data reconstruction. In Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI\u20132012)."},{"key":"S1351324918000414_ref038","doi-asserted-by":"crossref","unstructured":"Hong K. , Marcus M. , and Nenkova A. 2015. System combination for multi-document summarization. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP-2015), pp. 107\u201317.","DOI":"10.18653\/v1\/D15-1011"},{"key":"S1351324918000414_ref002","doi-asserted-by":"crossref","unstructured":"Amini M. , and Usunier N. 2009. Incorporating prior knowledge into a transductive ranking algorithm for multi-document summarization. In Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, pp. 704\u20135.","DOI":"10.1145\/1571941.1572087"},{"key":"S1351324918000414_ref083","doi-asserted-by":"crossref","unstructured":"Schluter N. , and Sogaard A. 2015. Unsupervised extractive summarization via coverage maximization with syntactic and semantic concepts. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (ACL-2015), vol. 2, pp. 840\u20134.","DOI":"10.3115\/v1\/P15-2138"},{"key":"S1351324918000414_ref049","doi-asserted-by":"crossref","unstructured":"Lin C. , Cao G. , Gao J. , and Nie J. 2006. An information-theoretic approach to automatic evaluation of summaries. In Proceedings of the Main Conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, ACL, pp. 463\u201370.","DOI":"10.3115\/1220835.1220894"},{"key":"S1351324918000414_ref029","doi-asserted-by":"crossref","unstructured":"Galley M. 2006. A skip-chain conditional random field for ranking meeting utterances by importance. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, ACL, pp. 364\u201372.","DOI":"10.3115\/1610075.1610126"},{"key":"S1351324918000414_ref013","unstructured":"Cao Z. , Wei F. , Dong L. , Li S. , and Zhou M. 2015. Ranking with recursive neural jnetworks and its application to multi-document summarization. In Proceedings of the 29th AAAI Conference on Artificial Intelligence, pp. 2153\u20132159."},{"key":"S1351324918000414_ref015","unstructured":"Christensen J. , Soderland S. , and Etzioni O. 2013. Towards coherent multi-document summarization. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1163\u201373."},{"key":"S1351324918000414_ref032","doi-asserted-by":"crossref","unstructured":"Gupta A. , Kaur M. , Singh A. , Goel A. , and Mirkin S. 2014. Text summarization through entailment-based minimum vertex cover. In Proceedings of the Third Joint Conference on Lexical and Computational Semantics (SEM-2014), pp. 75\u201380.","DOI":"10.3115\/v1\/S14-1010"},{"key":"S1351324918000414_ref014","doi-asserted-by":"crossref","unstructured":"Cao Z. , Wei F. , Li S. , Li W. , Zhou M. , and Wang H. 2015. Learning summary prior representation for extractive summarization. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Short Papers), Beijing: China, pp. 829\u201333.","DOI":"10.3115\/v1\/P15-2136"},{"key":"S1351324918000414_ref046","doi-asserted-by":"crossref","unstructured":"Li C. , Liu Y. , and Zhao L. 2015. Improving update summarization via supervised ILP and sentence reranking. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (HLT-NAACL-2015), pp. 1317\u201322.","DOI":"10.3115\/v1\/N15-1145"},{"key":"S1351324918000414_ref056","unstructured":"Lopez C. , Prince V. , and Roche M. 2011. Automatic titling of articles using position and statistical information. In Proceedings of the Recent Advances in Natural Language Processing (RANLP-2011), pp. 727\u201332."},{"key":"S1351324918000414_ref006","unstructured":"Bentivogli L. , Clark P. , Dagan I. , and Giampiccolo D. 2009. The fifth PASCAL recognizing textual entailment challenge. In Proceedings of the Text Analysis Conference."},{"key":"S1351324918000414_ref077","doi-asserted-by":"publisher","DOI":"10.1162\/089120102762671927"},{"key":"S1351324918000414_ref064","unstructured":"Mason R. , and Charniak E. 2011. Extractive multi-document summaries should explicitly not contain document-specific content. In Proceedings of the Workshop on Automatic Summarization for Different Genres, Media, and Languages, ACL, pp. 49\u201354."},{"key":"S1351324918000414_ref057","doi-asserted-by":"publisher","DOI":"10.1145\/984321.984323"},{"key":"S1351324918000414_ref075","doi-asserted-by":"publisher","DOI":"10.1017\/S1351324913000351"},{"key":"S1351324918000414_ref072","unstructured":"Orasan C. , Pekar V. , and Hasler L. 2004. A comparison of summarisation methods based on term specificity estimation. In International Conference on Language Resources and Evaluation (LREC-2004)."},{"key":"S1351324918000414_ref070","doi-asserted-by":"crossref","unstructured":"Nenkova A. , Vanderwende L. , and McKeown K. 2006. A compositional context sensitive multi-document summarizer: exploring the factors that influence summarization. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, pp. 573\u201380.","DOI":"10.1145\/1148170.1148269"},{"key":"S1351324918000414_ref019","doi-asserted-by":"publisher","DOI":"10.1007\/11736790_9"},{"key":"S1351324918000414_ref085","doi-asserted-by":"crossref","unstructured":"Silva G. , Ferreira R. , Dueire Lins R. , Cabral L. , Oliveira H. , Simske S. , and Riss M. 2015. Automatic text document summarization based on machine learning. In Proceedings of the ACM Symposium on Document Engineering, ACM, pp. 191\u20134.","DOI":"10.1145\/2682571.2797099"},{"key":"S1351324918000414_ref068","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-3223-4_3"},{"key":"S1351324918000414_ref059","unstructured":"Madnani N. , Zajic D. , Dorr B. , Ayan N. , and Lin J. 2007. Multiple alternative sentence compressions for automatic text summarization. In Proceedings of Document Understanding Conference (DUC-2007)."},{"key":"S1351324918000414_ref041","first-page":"40","volume-title":"Proceedings of the 2nd International IEEE Conference on Intelligent Systems","author":"Kaikhah","year":"2004"},{"key":"S1351324918000414_ref062","unstructured":"Marcu D. 1997. From discourse structures to text summaries. In Proceedings of the Association of Computer Linguistics (ACL) Workshop on Intelligent Scalable Text Summarization, pp. 82\u20138."},{"key":"S1351324918000414_ref093","doi-asserted-by":"crossref","unstructured":"Yasunaga M. , Zhang R. , Meelu K. , Pareek A. , Srinivasan K. , and Radev D. 2017. Graph-based neural multi-document summarization. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL-2017), Vancouver, Canada. pp. 452\u201362.","DOI":"10.18653\/v1\/K17-1045"},{"key":"S1351324918000414_ref001","unstructured":"Almeida M. , and Martins A. 2013. Fast and robust compressive summarization with dual decomposition and multi-task learning. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 196\u2013206."},{"key":"S1351324918000414_ref003","first-page":"142","volume-title":"Proceedings of the European Conference on Information Retrieval","author":"Amini","year":"2005"},{"key":"S1351324918000414_ref004","doi-asserted-by":"crossref","unstructured":"Baumel T. , Cohen R. , and Elhadad M. 2014. Query-chain focused summarization. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 913\u201322.","DOI":"10.3115\/v1\/P14-1086"},{"key":"S1351324918000414_ref005","doi-asserted-by":"publisher","DOI":"10.1147\/rd.24.0354"},{"key":"S1351324918000414_ref008","first-page":"10","volume-title":"Proceedings of the Association for Computational Linguistics (ACL) workshop on intelligent scalable text summarization","author":"Brazilay","year":"1997"},{"key":"S1351324918000414_ref009","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.1109\/TASL.2013.2253098","article-title":"Ranking through clustering: an integrated approach to multi-document summarization","volume":"21","author":"Cai","year":"2013","journal-title":"IEEE Transactions on Audio, Speech, and Language Processing"},{"key":"S1351324918000414_ref034","unstructured":"Hovy E. , and Lin C. 1998. Automated text summarization and the SUMMARIST system. In Proceedings of a workshop on held at Baltimore Maryland, ACL, pp. 197\u2013214."},{"key":"S1351324918000414_ref054","doi-asserted-by":"crossref","unstructured":"Litvak M. , Vanetik N. , and Last M. 2015. Krimping texts for better summarization. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1931\u20135.","DOI":"10.18653\/v1\/D15-1223"},{"key":"S1351324918000414_ref087","doi-asserted-by":"crossref","unstructured":"Takamura H. , and Okumura M. 2009. Text summarization model based on maximum coverage problem and its variant. In Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, ACL, pp. 781\u20139.","DOI":"10.3115\/1609067.1609154"},{"key":"S1351324918000414_ref027","doi-asserted-by":"crossref","unstructured":"Filatova E. , and Hatzivassiloglou V. 2004. A formal model for information selection in multi-sentence text extraction. In Proceedings of the 20th International Conference on Computational Linguistics, ACL, p. 397.","DOI":"10.3115\/1220355.1220412"},{"key":"S1351324918000414_ref011","unstructured":"Cao Z. , Li W. , Li S. , and Wei F. 2017. Improving multi-document summarization via text classification. In Proceedings of the 31st AAAI Conference on Artificial Intelligence, pp. 3053\u20139."},{"key":"S1351324918000414_ref012","first-page":"547","volume-title":"Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers (COLING-2016)","author":"Cao","year":"2016"},{"key":"S1351324918000414_ref018","unstructured":"Conroy J. , Schlesinger J. , and O\u2019Leary D. 2007. Classy 2007 at duc 2007. In Proceedings of the Document Understanding Conference."},{"key":"S1351324918000414_ref020","unstructured":"Das D. , and Martins A 2007. A Survey on Automatic Text Summarization. Literature Survey for the Language and Statistics II course at CMU 4, pp. 192\u20135."},{"key":"S1351324918000414_ref094","doi-asserted-by":"crossref","unstructured":"Zhou L. , and Hovy E. 2003. A web-trained extraction summarization system. In Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, ACL, vol. 1, pp. 205\u201311.","DOI":"10.3115\/1073445.1073482"},{"key":"S1351324918000414_ref021","unstructured":"Daume H. , and Marcu D. 2005. Bayesian multi-document summarization at MSE. In ACL 2005, Workshop on Multilingual Summarization Evaluation (MSE)."},{"key":"S1351324918000414_ref022","doi-asserted-by":"crossref","unstructured":"Daume H. III , and Marcu D. 2006. Bayesian query-focused summarization. In Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, ACL, pp. 305\u201312.","DOI":"10.3115\/1220175.1220214"},{"key":"S1351324918000414_ref023","doi-asserted-by":"crossref","unstructured":"Donaway R. , Drummey K. , and Mather L. 2000. A comparison of rankings produced by summarization evaluation measures. In Proceedings of the 2000 NAACL-ANLP Workshop on Automatic Summarization, vol. 4, pp. 69\u201378.","DOI":"10.3115\/1117575.1117583"},{"key":"S1351324918000414_ref024","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2007.01.003"},{"key":"S1351324918000414_ref026","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1523"},{"key":"S1351324918000414_ref028","doi-asserted-by":"crossref","unstructured":"Fuentes M. , Alfonseca E. , and Rodriguez H. 2007. Support vector machines for query-focused summarization trained and evaluated on pyramid data. In Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions, ACL, pp. 57\u201360.","DOI":"10.3115\/1557769.1557788"},{"key":"S1351324918000414_ref030","doi-asserted-by":"crossref","unstructured":"Gong Y. , and Liu X. 2001. Generic text summarization using relevance measure and latent semantic analysis. In Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 19\u201325.","DOI":"10.1145\/383952.383955"},{"key":"S1351324918000414_ref031","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-35542-4_9"},{"key":"S1351324918000414_ref036","unstructured":"Hirao T. , Yoshida Y. , Nishino M. , Yasuda N. , and Nagata M. 2013. Single-document summarization as a tree knapsack problem. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP-2013), vol. 13, pp. 1515\u201320."},{"key":"S1351324918000414_ref037","first-page":"1","volume-title":"Proceedings of the 19th International Conference on Computational Linguistics","author":"Hirao","year":"2002"},{"key":"S1351324918000414_ref050","unstructured":"Lin S. , and Chen B. 2010. A risk minimization framework for extractive speech summarization. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, ACL, pp. 79\u201387."},{"key":"S1351324918000414_ref039","doi-asserted-by":"crossref","unstructured":"Hong K. , and Nenkova A. 2014. Improving the estimation of word importance for news multi-document summarization. In Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics (EACL-2014), pp. 712\u201321.","DOI":"10.3115\/v1\/E14-1075"},{"key":"S1351324918000414_ref042","unstructured":"Knight K. , and Marcu D. 2000. Statistics-based summarization-step one: sentence compression. In Proceedings of the 17th National Conference on Artificial Intelligence and 12th Conference on Innovative Applications of Artificial Intelligence (AAAI\/IAAI-2000), pp. 703\u201310."},{"key":"S1351324918000414_ref082","unstructured":"Saggion H. , and Gaizauskas R. 2004. Multi-document summarization by cluster\/profile relevance and redundancy removal. In Proceedings of the Document Understanding Conference (DUC-2004)."},{"key":"S1351324918000414_ref043","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(02)00222-9"},{"key":"S1351324918000414_ref044","doi-asserted-by":"publisher","DOI":"10.1093\/comjnl\/bxp124"},{"key":"S1351324918000414_ref033","doi-asserted-by":"crossref","unstructured":"Haghighi A. , and Vanderwende L. 2009. Exploring content models for multi-document summarization. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, ACL, pp. 362\u201370.","DOI":"10.3115\/1620754.1620807"},{"key":"S1351324918000414_ref045","unstructured":"Li P. , Bing L. , Lam W. , Li H. , and Liao Y. 2015. Reader-aware multi-document summarization via sparse coding. In IJCAI, pp. 1270\u20131276."},{"key":"S1351324918000414_ref048","unstructured":"Lin C. 2004. Rouge: a package for automatic evaluation of summaries. In Text Summarization Branches Out: Proceedings of the ACL-04 Workshop, vol. 8."},{"key":"S1351324918000414_ref074","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2010.03.005"},{"key":"S1351324918000414_ref010","doi-asserted-by":"publisher","DOI":"10.1016\/j.csl.2015.11.004"},{"key":"S1351324918000414_ref051","doi-asserted-by":"crossref","unstructured":"Lin C. , and Hovy E. 1997. Identifying topics by position. In Proceedings of the 5th Conference on Applied Natural Language Processing, ACL, pp. 283\u201390.","DOI":"10.3115\/974557.974599"},{"key":"S1351324918000414_ref052","first-page":"71","volume-title":"Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology","author":"Lin","year":"2003"},{"key":"S1351324918000414_ref055","unstructured":"Liu F. , and Liu Y. 2009. From extractive to abstractive meeting summaries: can it be done by sentence compression?. In Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, ACL, pp. 261\u20134."},{"key":"S1351324918000414_ref063","doi-asserted-by":"crossref","unstructured":"Martins A. F. , and Smith N. A. 2009. Summarization with a joint model for sentence extraction and compression. In Proceedings of the Workshop on Integer Linear Programming for Natural Langauge Processing, pp. 1\u20139.","DOI":"10.3115\/1611638.1611639"},{"key":"S1351324918000414_ref065","unstructured":"Metzler D. , and Kanungo T. 2008. Machine learned sentence selection strategies for query-biased summarization. In SIGIR Learning to Rank Workshop, pp. 40\u20137."},{"key":"S1351324918000414_ref067","doi-asserted-by":"publisher","DOI":"10.1561\/1500000015"},{"key":"S1351324918000414_ref069","unstructured":"Nenkova A. , and Passonneau R. 2004. Evaluating content selection in summarization: the pyramid method. In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL-2004): Main Proceedings, ACL, pp. 145\u201352."},{"key":"S1351324918000414_ref071","unstructured":"Nishikawa H. , Hirao T. , Makino T. , and Matsuo Y. 2012. Text summarization model based on redundancy-constrained knapsack problem. In Proceedings of the International Conference on Computational Linguistics (COLING-2012) (Posters), pp. 893\u2013902."},{"key":"S1351324918000414_ref073","first-page":"1","volume-title":"Proceedings of the ACL-02 Workshop on Automatic Summarization","author":"Osborne","year":"2002"},{"key":"S1351324918000414_ref076","first-page":"43","article-title":"Automatic abstracting research at chemical abstracts service","volume":"15","author":"Pollock","year":"1999","journal-title":"Advances in Automatic Text Summarization"},{"key":"S1351324918000414_ref078","doi-asserted-by":"crossref","unstructured":"Radev D. , Teufel S. , Saggion H. , Lam W. , Blitzer J. , Qi H. , Elebi A. , Liu D. , and Drabek E. 2003. Evaluation challenges in large scale document summarization. In Proceedings of the 41st Annual Meeting on Association for Computational Linguistics (ACL-2003), pp. 375\u201382.","DOI":"10.3115\/1075096.1075144"},{"key":"S1351324918000414_ref081","doi-asserted-by":"publisher","DOI":"10.1108\/00220410410560582"},{"key":"S1351324918000414_ref084","unstructured":"Shen D. , Sun J. , Li H. , Yang Q. , and Chen Z. 2007. Document summarization using conditional random fields. In Proceedings of the 20th International Joint Conference on Artifical Intelligence (IJCAI-2007), vol. 7, pp. 2862\u20137."},{"key":"S1351324918000414_ref086","doi-asserted-by":"crossref","unstructured":"Suzuki Y. , and Fukumoto F. 2014. Detection of topic and its extrinsic evaluation through multi-document summarization. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL-2014), vol. 2, pp. 241\u20136.","DOI":"10.3115\/v1\/P14-2040"},{"key":"S1351324918000414_ref089","unstructured":"Toutanova K. , Brockett C. , Gamon M. , Jagarlamudi J. , Suzuki H. , and Vanderwende L. 2007. The pythy summarization system: microsoft research at duc 2007. In Proceedings of the Document Understanding Conference (DUC-2007), vol. 2007."},{"key":"S1351324918000414_ref092","unstructured":"Woodsend K. , and Lapata M. 2012. Multiple aspect summarization using integer linear programming. In Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 233\u201343."}],"container-title":["Natural Language Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S1351324918000414","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,4,12]],"date-time":"2019-04-12T20:53:04Z","timestamp":1555102384000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S1351324918000414\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10,31]]},"references-count":94,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,1]]}},"alternative-id":["S1351324918000414"],"URL":"https:\/\/doi.org\/10.1017\/s1351324918000414","relation":{},"ISSN":["1351-3249","1469-8110"],"issn-type":[{"value":"1351-3249","type":"print"},{"value":"1469-8110","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,10,31]]}}}