Summarizing Community-based Question-Answer Pairs

TL;DR

This paper introduces a new task for summarizing community question-answer pairs, creates a benchmark dataset, and evaluates various summarization methods to address key challenges like sentence-type transfer and deduplication.

Contribution

The paper proposes the CQA summarization task, develops the CoQASUM dataset, and establishes baseline methods, highlighting unique challenges in the domain.

Findings

Identified sentence-type transfer as a key challenge.

Demonstrated the effectiveness of the DedupLED baseline.

Provided publicly available data and code for future research.

Abstract

Community-based Question Answering (CQA), which allows users to acquire their desired information, has increasingly become an essential component of online services in various domains such as E-commerce, travel, and dining. However, an overwhelming number of CQA pairs makes it difficult for users without particular intent to find useful information spread over CQA pairs. To help users quickly digest the key information, we propose the novel CQA summarization task that aims to create a concise summary from CQA pairs. To this end, we first design a multi-stage data annotation process and create a benchmark dataset, CoQASUM, based on the Amazon QA corpus. We then compare a collection of extractive and abstractive summarization methods and establish a strong baseline approach DedupLED for the CQA summarization task. Our experiment further confirms two key challenges, sentence-type transfer and deduplication removal, towards the CQA summarization task. Our data and code are publicly available.