# Summarizing Community-based Question-Answer Pairs

arXiv: https://arxiv.org/abs/2211.09892 · HTML: https://mlsanity.com/paper/2211.09892/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 30 references that are in the map.
- First posted on arXiv 17 Nov 2022.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting](https://mlsanity.com/paper/1805.11080/) (2018)
- [ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining](https://mlsanity.com/paper/2106.00829/) (2021)
- [Scoring Sentence Singletons and Pairs for Abstractive Summarization](https://mlsanity.com/paper/1906.00077/) (2019)
- [TWEETSUMM -- A Dialog Summarization Dataset for Customer Service](https://mlsanity.com/paper/2111.11894/) (2021)
- [Addressing Complex and Subjective Product-Related Queries with Customer Reviews](https://mlsanity.com/paper/1512.06863/) (2015)
- [Joint Learning of Answer Selection and Answer Summary Generation in Community Question Answering](https://mlsanity.com/paper/1911.09801/) (2019)
- [EmailSum: Abstractive Email Thread Summarization](https://mlsanity.com/paper/2107.14691/) (2021)
- [Comparative Opinion Summarization via Collaborative Decoding](https://mlsanity.com/paper/2110.07520/) (2021)
- [Modeling Ambiguity, Subjectivity, and Diverging Viewpoints in Opinion Question Answering Systems](https://mlsanity.com/paper/1610.08095/) (2016)
- [CQASUMM: Building References for Community Question Answering Summarization Corpora](https://mlsanity.com/paper/1811.04884/) (2018)
- [Neural Abstractive Summarization with Structural Attention](https://mlsanity.com/paper/2004.09739/) (2020)
- [AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization](https://mlsanity.com/paper/2111.06474/) (2021)
- [Beyond Opinion Mining: Summarizing Opinions of Customer Reviews](https://mlsanity.com/paper/2206.01543/) (2022)

## Built on by (most cited first, 2 of 2)

- [Aspect-oriented Consumer Health Answer Summarization](https://mlsanity.com/paper/2405.06295/) (2024)
- [CTISum: A New Benchmark Dataset For Cyber Threat Intelligence Summarization](https://mlsanity.com/paper/2408.06576/) (2024)

## Abstract (arXiv)

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.