Can I read Multi-Perspective Abstractive Answer Summarization on EtoBox?

Multi-Perspective Abstractive Answer Summarization by Fabbri, Alexander R.; Wu, Xiaojian; Iyer, Srini; Diab, Mona is a scholarly article available to read on EtoBox.

What is Multi-Perspective Abstractive Answer Summarization about?

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.

Author

Fabbri, Alexander R.; Wu, Xiaojian; Iyer, Srini; Diab, Mona

Published

2021

Language

EN