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        <title>Diversity driven attention model for query-based abstractive summarization</title>
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        <namePart type="given">Preksha</namePart>
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        <namePart type="given">Mitesh</namePart>
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    <name type="personal">
        <namePart type="given">Balaraman</namePart>
        <namePart type="family">Ravindran</namePart>
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            <title>Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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    <abstract>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 encode-attend-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.</abstract>
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%0 Conference Proceedings
%T Diversity driven attention model for query-based abstractive summarization
%A Nema, Preksha
%A Khapra, Mitesh M.
%A Laha, Anirban
%A Ravindran, Balaraman
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F nema-etal-2017-diversity
%X 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 encode-attend-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.
%R 10.18653/v1/P17-1098
%U https://aclanthology.org/P17-1098
%U https://doi.org/10.18653/v1/P17-1098
%P 1063-1072

Markdown (Informal)

[Diversity driven attention model for query-based abstractive summarization](https://aclanthology.org/P17-1098) (Nema et al., ACL 2017)

• Diversity driven attention model for query-based abstractive summarization (Nema et al., ACL 2017)

ACL

• Preksha Nema, Mitesh M. Khapra, Anirban Laha, and Balaraman Ravindran. 2017. Diversity driven attention model for query-based abstractive summarization. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1063–1072, Vancouver, Canada. Association for Computational Linguistics.