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http://hdl.handle.net/10397/105724
Title: | AttSum : joint learning of focusing and summarization with neural attention | Authors: | Cao, Z Li, W Li, S Wei, F Li, Y |
Issue Date: | 2016 | Source: | In The 26th International Conference on Computational Linguistics: Proceedings of COLING 2016: Technical Papers, p. 547-556 | Abstract: | Query relevance ranking and sentence saliency ranking are the two main tasks in extractive query-focused summarization. Previous supervised summarization systems often perform the two tasks in isolation. However, since reference summaries are the trade-off between relevance and saliency, using them as supervision, neither of the two rankers could be trained well. This paper proposes a novel summarization system called AttSum, which tackles the two tasks jointly. It automatically learns distributed representations for sentences as well as the document cluster. Meanwhile, it applies the attention mechanism to simulate the attentive reading of human behavior when a query is given. Extensive experiments are conducted on DUC query-focused summarization benchmark datasets. Without using any hand-crafted features, AttSum achieves competitive performance. We also observe that the sentences recognized to focus on the query indeed meet the query need. | Publisher: | Association for Computational Linguistics (ACL) | ISBN: | 978-4-87974-702-0 | Description: | 26th International Conference on Computational Linguistics, December 11-16, 2016, Osaka, Japan | Rights: | Copyright of each paper stays with the respective authors (or their employers). Posted with permission of the author. |
Appears in Collections: | Conference Paper |
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