Conference Proceedings
Inducing document structure for aspect-based summarization
L Frermann, A Klementiev
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics | Association for Computational Linguistics | Published : 2019
DOI: 10.18653/v1/P19-1630
Abstract
Automatic summarization is typically treated as a 1-to-1 mapping from document to summary. Documents such as news articles, however, are structured and often cover multiple topics or aspects; and readers may be interested in only some of them. We tackle the task of aspect-based summarization, where, given a document and a target aspect, our models generate a summary centered around the aspect. We induce latent document structure jointly with an abstractive summarization objective, and train our models in a scalable synthetic setup. In addition to improvements in summarization over topic-agnostic baselines, we demonstrate the benefit of the learnt document structure: we show that our models (..
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