Year: 2017
Type: conference-paper
Abstract: ive 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.
DOI: 10.18653/v1/p17-1098
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2611254175