AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization

TL;DR

This paper introduces AnswerSumm, a curated dataset of community question answering threads, along with a comprehensive pipeline for answer summarization that includes data annotation, multi-perspective grouping, and evaluation of models.

Contribution

It provides a novel, professionally curated dataset and a multi-stage pipeline for answer summarization, including new unsupervised data augmentation and reinforcement learning techniques.

Findings

State-of-the-art models benchmarked on the dataset.

Unsupervised data augmentation improves summarization performance.

Reinforcement learning rewards enhance factual consistency and coverage.

Abstract

Community Question Answering (CQA) fora such as Stack Overflow and Yahoo! Answers contain a rich resource of answers to a wide range of community-based questions. Each question thread can receive a large number of answers with different perspectives. One goal of answer summarization is to produce a summary that reflects the range of answer perspectives. A major obstacle for this task is the absence of a dataset to provide supervision for producing such summaries. Recent works propose heuristics to create such data, but these are often noisy and do not cover all answer perspectives present. This work introduces a novel dataset of 4,631 CQA threads for answer summarization curated by professional linguists. Our pipeline gathers annotations for all subtasks of answer summarization, including relevant answer sentence selection, grouping these sentences based on perspectives, summarizing each perspective, and producing an overall summary. We analyze and benchmark state-of-the-art models on these subtasks and introduce a novel unsupervised approach for multi-perspective data augmentation that boosts summarization performance according to automatic evaluation. Finally, we propose reinforcement learning rewards to improve factual consistency and answer coverage and analyze areas for improvement.