{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T10:43:39Z","timestamp":1759401819189,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819981441"},{"type":"electronic","value":"9789819981458"}],"license":[{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-8145-8_43","type":"book-chapter","created":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T23:02:30Z","timestamp":1701039750000},"page":"567-578","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["LenANet: A Length-Controllable Attention Network for\u00a0Source Code Summarization"],"prefix":"10.1007","author":[{"given":"Peng","family":"Chen","sequence":"first","affiliation":[]},{"given":"Shaojuan","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Ziqiang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Jiarui","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xiaowang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Zhiyong","family":"Feng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,27]]},"reference":[{"key":"43_CR1","doi-asserted-by":"crossref","unstructured":"Ahmad, W.U., Chakraborty, S., Ray, B., Chang, K.: A transformer-based approach for source code summarization. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5\u201310, 2020, pp. 4998\u20135007. Association for Computational Linguistics (2020)","DOI":"10.18653\/v1\/2020.acl-main.449"},{"key":"43_CR2","unstructured":"Ahmad, W.U., Chakraborty, S., Ray, B., Chang, K.: Unified pre-training for program understanding and generation. CoRR abs\/2103.06333"},{"key":"43_CR3","unstructured":"Allamanis, M., Peng, H., Sutton, C.: A convolutional attention network for extreme summarization of source code. In: Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19\u201324, 2016. JMLR Workshop and Conference Proceedings, vol. 48, pp. 2091\u20132100. JMLR.org"},{"key":"43_CR4","unstructured":"Dathathri, S., et al.: Plug and play language models: a simple approach to controlled text generation. In: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26\u201330, 2020. OpenReview.net"},{"key":"43_CR5","doi-asserted-by":"crossref","unstructured":"Feng, Z., et al.: Codebert: a pre-trained model for programming and natural languages. In: Findings of the Association for Computational Linguistics: EMNLP 2020, Online Event, 16\u201320 November 2020. Findings of ACL, vol. EMNLP 2020, pp. 1536\u20131547. Association for Computational Linguistics (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.139"},{"key":"43_CR6","unstructured":"Feng, Z., et al.: Codebert: a pre-trained model for programming and natural languages. CoRR abs\/2002.08155"},{"key":"43_CR7","unstructured":"Holtzman, A., Buys, J., Forbes, M., Choi, Y.: The curious case of neural text degeneration. CoRR abs\/1904.09751"},{"key":"43_CR8","doi-asserted-by":"crossref","unstructured":"Iyer, S., Konstas, I., Cheung, A., Zettlemoyer, L.: Summarizing source code using a neural attention model. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, August 7\u201312, 2016, Berlin, Germany, Volume 1: Long Papers. The Association for Computer Linguistics (2016)","DOI":"10.18653\/v1\/P16-1195"},{"key":"43_CR9","unstructured":"Keskar, N.S., McCann, B., Varshney, L.R., Xiong, C., Socher, R.: CTRL: a conditional transformer language model for controllable generation. CoRR abs\/1909.05858"},{"key":"43_CR10","doi-asserted-by":"crossref","unstructured":"LeClair, A., Haque, S., Wu, L., McMillan, C.: Improved code summarization via a graph neural network. In: ICPC \u201920: 28th International Conference on Program Comprehension, Seoul, Republic of Korea, July 13\u201315, 2020, pp. 184\u2013195. ACM (2020)","DOI":"10.1145\/3387904.3389268"},{"key":"43_CR11","unstructured":"Lester, B., Al-Rfou, R., Constant, N.: The power of scale for parameter-efficient prompt tuning. CoRR abs\/2104.08691"},{"key":"43_CR12","unstructured":"Liu, S., Chen, Y., Xie, X., Siow, J.K., Liu, Y.: Retrieval-augmented generation for code summarization via hybrid GNN. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3\u20137, 2021. OpenReview.net"},{"key":"43_CR13","unstructured":"Liu, Y., et al.: Roberta: a robustly optimized BERT pretraining approach. CoRR abs\/1907.11692"},{"key":"43_CR14","unstructured":"Lu, S., et al.: Codexglue: a machine learning benchmark dataset for code understanding and generation. CoRR abs\/2102.04664"},{"issue":"2","key":"43_CR15","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1109\/TSE.2015.2465386","volume":"42","author":"PW McBurney","year":"2016","unstructured":"McBurney, P.W., McMillan, C.: Automatic source code summarization of context for Java methods. IEEE Trans. Softw. Eng. 42(2), 103\u2013119 (2016)","journal-title":"IEEE Trans. Softw. Eng."},{"key":"43_CR16","doi-asserted-by":"crossref","unstructured":"Moreno, L., Aponte, J., Sridhara, G., Marcus, A., Pollock, L.L., Vijay-Shanker, K.: Automatic generation of natural language summaries for java classes. In: IEEE 21st International Conference on Program Comprehension, ICPC 2013, San Francisco, CA, USA, 20\u201321 May, 2013, pp. 23\u201332. IEEE Computer Society (2013)","DOI":"10.1109\/ICPC.2013.6613830"},{"key":"43_CR17","unstructured":"Rozi\u00e8re, B., Lachaux, M.A., Szafraniec, M., Lample, G.: Dobf: A deobfuscation pre-training objective for programming languages. In: NeurIPS"},{"key":"43_CR18","doi-asserted-by":"crossref","unstructured":"Shido, Y., Kobayashi, Y., Yamamoto, A., Miyamoto, A., Matsumura, T.: Automatic source code summarization with extended tree-lstm. In: International Joint Conference on Neural Networks, IJCNN 2019 Budapest, Hungary, July 14\u201319, 2019, pp. 1\u20138. IEEE (2019)","DOI":"10.1109\/IJCNN.2019.8851751"},{"key":"43_CR19","doi-asserted-by":"crossref","unstructured":"Sridhara, G., Hill, E., Muppaneni, D., Pollock, L.L., Vijay-Shanker, K.: Towards automatically generating summary comments for java methods. In: ASE 2010, 25th IEEE\/ACM International Conference on Automated Software Engineering, Antwerp, Belgium, September 20\u201324, 2010, pp. 43\u201352. ACM (2010)","DOI":"10.1145\/1858996.1859006"},{"key":"43_CR20","doi-asserted-by":"crossref","unstructured":"Sun, W., et al.: An extractive-and-abstractive framework for source code summarization. CoRR abs\/2206.07245 (2022)","DOI":"10.1145\/3632742"},{"key":"43_CR21","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. CoRR abs\/1409.3215"},{"key":"43_CR22","doi-asserted-by":"crossref","unstructured":"Tang, Z., Li, C., Ge, J., Shen, X., Zhu, Z., Luo, B.: Ast-transformer: encoding abstract syntax trees efficiently for code summarization. In: 36th IEEE\/ACM International Conference on Automated Software Engineering, ASE 2021, Melbourne, Australia, November 15\u201319, 2021, pp. 1193\u20131195. IEEE (2021)","DOI":"10.1109\/ASE51524.2021.9678882"},{"key":"43_CR23","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations"},{"key":"43_CR24","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, W., Joty, S., Hoi., S.C.: Codet 5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021","DOI":"10.18653\/v1\/2021.emnlp-main.685"},{"key":"43_CR25","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wang, X., Zhang, H., Sun, H., Liu, X.: Retrieval-based neural source code summarization. In: ICSE \u201920: 42nd International Conference on Software Engineering, Seoul, South Korea, 27 June - 19 July, 2020, pp. 1385\u20131397. ACM (2020)","DOI":"10.1145\/3377811.3380383"},{"key":"43_CR26","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Shen, X., Bi, W., Aizawa, A.: Unsupervised rewriter for multi-sentence compression. In: Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, pp. 2235\u20132240. Association for Computational Linguistics (2019)","DOI":"10.18653\/v1\/P19-1216"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8145-8_43","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T19:01:41Z","timestamp":1710356501000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8145-8_43"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,27]]},"ISBN":["9789819981441","9789819981458"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8145-8_43","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023,11,27]]},"assertion":[{"value":"27 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"650","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.14","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.46","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}