Multi-Perspective Abstractive Answer Summarization
- URL: http://arxiv.org/abs/2104.08536v1
- Date: Sat, 17 Apr 2021 13:15:29 GMT
- Title: Multi-Perspective Abstractive Answer Summarization
- Authors: Alexander R. Fabbri, Xiaojian Wu, Srini Iyer, Mona Diab
- Abstract summary: Community Question Answering forums contain a rich resource of answers to a wide range of questions.
The goal of multi-perspective answer summarization is to produce a summary that includes all perspectives of the answer.
This work introduces a novel dataset creation method to automatically create multi-perspective, bullet-point abstractive summaries.
- Score: 76.10437565615138
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Community Question Answering (CQA) forums such as Stack Overflow and Yahoo!
Answers contain a rich resource of answers to a wide range of questions. Each
question thread can receive a large number of answers with different
perspectives. The goal of multi-perspective answer summarization is to produce
a summary that includes all perspectives of the answer. A major obstacle for
multi-perspective, abstractive answer summarization is the absence of a dataset
to provide supervision for producing such summaries. This work introduces a
novel dataset creation method to automatically create multi-perspective,
bullet-point abstractive summaries from an existing CQA forum. Supervision
provided by this dataset trains models to inherently produce multi-perspective
summaries. Additionally, to train models to output more diverse, faithful
answer summaries while retaining multiple perspectives, we propose a
multi-reward optimization technique coupled with a sentence-relevance
prediction multi-task loss. Our methods demonstrate improved coverage of
perspectives and faithfulness as measured by automatic and human evaluations
compared to a strong baseline.
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