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Abstract
We present a neural framework for opinion summarization from online product reviews which is knowledge-lean and only requires light supervision (e.g., in the form of product domain labels and user-provided ratings). Our method combines two weakly supervised components to identify salient opinions and form extractive summaries from multiple reviews: an aspect extractor trained under a multi-task objective, and a sentiment predictor based on multiple instance learning. We introduce an opinion summarization dataset that includes a training set of product reviews from six diverse domains and human-annotated development and test sets with gold standard aspect annotations, salience labels, and opinion summaries. Automatic evaluation shows significant improvements over baselines, and a largescale study indicates that our opinion summaries are preferred by human judges according to multiple criteria.1
Original language | English |
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Title of host publication | Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing |
Place of Publication | Brussels, Belgium |
Publisher | Association for Computational Linguistics |
Pages | 3675-3686 |
Number of pages | 12 |
Publication status | Published - Nov 2018 |
Event | 2018 Conference on Empirical Methods in Natural Language Processing - Square Meeting Center, Brussels, Belgium Duration: 31 Oct 2018 → 4 Nov 2018 http://emnlp2018.org/ |
Conference
Conference | 2018 Conference on Empirical Methods in Natural Language Processing |
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Abbreviated title | EMNLP 2018 |
Country/Territory | Belgium |
City | Brussels |
Period | 31/10/18 → 4/11/18 |
Internet address |
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