Summarizing Community-based Question-Answer Pairs

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 3798 - 3808 December 7-11, 2022 ©2022 Association for Computational Linguistics Summarizing Community-based Question-Answer Pairs Ting-Yao Hsu∗ Pennsylvania State University txh357@psu.edu Yoshi Suhara∗ Grammarly yoshi.suhara@grammarly.com Xiaolan Wang∗ Meta AI xiaolan@meta.com Abstract Community-based Question Answering (CQA), which allows users to acquire their desired in- formation, has increasingly become an essen- tial component of online services in various do- mains such as E-commerce, travel, and dining. However, an overwhelming number of CQA pairs makes it difficult for users without par- ticular 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 con- cise summary from CQA pairs. To this end, we first design a multi-stage data annotation process and create a benchmark dataset, CO- QASUM, based on the Amazon QA corpus. We then compare a collection of extractive and abstractive summarization methods and estab- lish a strong baseline approach DedupLED for the CQA summarization task. Our experiment further confirms two key challenges, sentence- type transfer and deduplication removal, to- wards the CQA summarization task. Our data and code are publicly available.1 1 Introduction Community-based Question Answering (CQA) en- ables users to post their questions and obtain an- swers from other users. With the increase in online services, CQA has become essential for various pur- poses, including online shopping, hotel/restaurant booking, and job searching. Many online platforms implement CQA features to help users acquire additional information about entities (e.g., prod- ucts, hotels, restaurants, and companies) of their interests. CQA complements customer reviews— another type of user-generated content, which pro- vide additional information about the entity but mostly focusing on user experiences and their opin- ions. ∗Work done while at Megagon Labs. 1https://github.com/megagonlabs/ qa-summarization Q: Is this actually a rigid board or more of a floppy mat? A: It is rigid.the main board is rigid,the two sides are semi. Q: Is this actually a rigid board or more of a floppy mat? A: The main area is very sturdy. Then there are two work area pads that are more flexible so when moving those I keep two hands on them. Q: how wide is each Side piece?" A: 16 inches wide (there are two). Q: will this mat hold 1000 piece puzzle? A: most certainly will. … … (omitted 27 QAs) Summary: This puzzle board comes with a rigid main board. You can arrange pieces in the middle and on two side pieces, and then pick up those side pieces to place them atop the middle area before folding the wings in. The dimension of the puzzle space is 32”x21.75”. The closed unit is almost the same size as the puzzle workspace (32”x21.75”). There are two 16” wide side inserts. The mat holds most 1000 pieces puzzles. It is too big to use on you lap and definitely needs a table. (a). QAs for a puzzle board product (Input) (b). Summary of …

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