Aspect-Oriented Summarization through Query-Focused Extraction
- URL: http://arxiv.org/abs/2110.08296v1
- Date: Fri, 15 Oct 2021 18:06:21 GMT
- Title: Aspect-Oriented Summarization through Query-Focused Extraction
- Authors: Ojas Ahuja, Jiacheng Xu, Akshay Gupta, Kevin Horecka, Greg Durrett
- Abstract summary: Real users' needs often fall more closely into aspects, broad topics in a dataset the user is interested in rather than specific queries.
We benchmark extractive query-focused training schemes, and propose a contrastive augmentation approach to train the model.
We evaluate on two aspect-oriented datasets and find this approach yields focused summaries, better than those from a generic summarization system.
- Score: 23.62412515574206
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A reader interested in a particular topic might be interested in summarizing
documents on that subject with a particular focus, rather than simply seeing
generic summaries produced by most summarization systems. While query-focused
summarization has been explored in prior work, this is often approached from
the standpoint of document-specific questions or on synthetic data. Real users'
needs often fall more closely into aspects, broad topics in a dataset the user
is interested in rather than specific queries. In this paper, we collect a
dataset of realistic aspect-oriented test cases, AspectNews, which covers
different subtopics about articles in news sub-domains. We then investigate how
query-focused methods, for which we can construct synthetic data, can handle
this aspect-oriented setting: we benchmark extractive query-focused training
schemes, and propose a contrastive augmentation approach to train the model. We
evaluate on two aspect-oriented datasets and find this approach yields (a)
focused summaries, better than those from a generic summarization system, which
go beyond simple keyword matching; (b) a system sensitive to the choice of
keywords.
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