The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Diversity driven attention model for query-based abstractive summarization
Open accessAbstract
Abstractive summarization aims to generate a shorter version of the document covering all the salient points in a compact and coherent fashion.On the other hand, query-based summarization highlights those points that are relevant in the context of a given query.The encodeattend-decode paradigm has achieved notable success in machine translation, extractive summarization, dialog systems, etc.But it suffers from the drawback of generation of repeated phrases.In this work we propose a model for the query-based summarization task based on the encode-attend-decode paradigm with two key additions (i) a query attention model (in addition to document attention model) which learns to focus on different portions of the query at different time steps (instead of using a static representation for the query) and (ii) a new diversity based attention model which aims to alleviate the problem of repeating phrases in the summary.In order to enable the testing of this model we introduce a new query-based summarization dataset building on debatepedia.Our experiments show that with these two additions the proposed model clearly outperforms vanilla encode-attend-decode models with a gain of 28% (absolute) in ROUGE-L scores.
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Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
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Abstractive Summarization: A Survey of the State of the Art
Lin, H., & Ng, V. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019815 · Cited by 120
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Mohankumar, A. K., Nema, P., Narasimhan, S., Khapra, M. M., Srinivasan, B. V., & Ravindran, B. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.387 · Cited by 91
Self-citationIncorporating External Knowledge into Machine Reading for Generative Question Answering
Bi, B., Wu, C., Yan, M., Wang, W., Xia, J., & Li, C. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1255 · Cited by 40
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Metadata
- Authors
- Nema, P., Khapra, M. M., Laha, A., & Ravindran, B.
- Venue
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2017
- Pages
- 1063-1072
- OpenAlex
- W2611254175
- Cited by
- 170
- References
- 2
- FWCI
- 9.78
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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