Semi-Supervised Extractive Question Summarizer Using Question-Answer Pairs and its Learning Methods-Reference-Cited by-同舟云学术

Semi-Supervised Extractive Question Summarizer Using Question-Answer Pairs and its Learning Methods

Published:2020-12-15 Issue:4 Volume:27 Page:825-852
ISSN:1340-7619
Container-title:Journal of Natural Language Processing
language:en
Short-container-title:Journal of Natural Language Processing

Author:

ISHIGAKI Tatsuya¹,Machida Kazuya²,Kobayashi Hayato³,TAKAMURA Hiroya¹⁴,OKUMURA Manabu⁴

Affiliation:

1. Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST)

2. Department of Information and Communications Engineering, School of Engineering, Tokyo Institute of Technology

3. Yahoo Japan Corporation

4. Laboratory for Future Interdisciplinary Research of Science and Technology (FIRST), Institute of Innovative Research (IIR), Tokyo Institute of Technology

Publisher

Association for Natural Language Processing

Link

https://www.jstage.jst.go.jp/article/jnlp/27/4/27_825/_pdf

Reference51 articles.

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2. Angelidis, S. and Lapata, M. (2018). “Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised.” In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP 2018), pp. 3675–3686. Association for Computational Linguistics.

3. Arumae, K. and Liu, F. (2018). “Reinforced Extractive Summarization with Question-Focused Rewards.” In Proceedings of 56th Annual Meeting of the Association for Computational Linguistics (ACL2018) Student Research Workshop, pp. 105–111. Association for Computational Linguistics.

4. Bhaskar, P. (2013). “Answering Questions from Multiple Documents—the Role of Multi-Document Summarization.” In Proceedings of the Recent Advances in Natural Language Processing 2013 (RANLP2014) Student Research Workshop, pp. 14–21.

5. Bollacker, K., Evans, C., Paritosh, P., Sturge, T., and Taylor, J. (2008). “Freebase: A Collaboratively Created Graph Database for Structuring Human Knowledge.” In Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data (SIGMOD2008), pp. 1247–1250. Association for Computational Machinery.