# QA-Align: Representing Cross-Text Content Overlap by Aligning Question-Answer Propositions

arXiv: https://arxiv.org/abs/2109.12655 · HTML: https://mlsanity.com/paper/2109.12655/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 48 references that are in the map.
- First posted on arXiv 26 Sep 2021.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model](https://mlsanity.com/paper/1906.01749/) (2019)
- [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019)
- [Constructing Datasets for Multi-hop Reading Comprehension Across Documents](https://mlsanity.com/paper/1710.06481/) (2017)
- [Coreferential Reasoning Learning for Language Representation](https://mlsanity.com/paper/2004.06870/) (2020)
- [Large-Scale QA-SRL Parsing](https://mlsanity.com/paper/1805.05377/) (2018)
- [Toward Abstractive Summarization Using Semantic Representations](https://mlsanity.com/paper/1805.10399/) (2018)
- [Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution](https://mlsanity.com/paper/1906.01753/) (2019)
- [Abstract Meaning Representation for Multi-Document Summarization](https://mlsanity.com/paper/1806.05655/) (2018)
- [Scoring Sentence Singletons and Pairs for Abstractive Summarization](https://mlsanity.com/paper/1906.00077/) (2019)
- [Multi-Hop Paragraph Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/1906.06606/) (2019)
- [Controlled Crowdsourcing for High-Quality QA-SRL Annotation](https://mlsanity.com/paper/1911.03243/) (2019)
- [DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion](https://mlsanity.com/paper/1902.10526/) (2019)
- [Analyzing Sentence Fusion in Abstractive Summarization](https://mlsanity.com/paper/1910.00203/) (2019)
- [Understanding Points of Correspondence between Sentences for Abstractive Summarization](https://mlsanity.com/paper/2006.05621/) (2020)
- [Streamlining Cross-Document Coreference Resolution: Evaluation and Modeling](https://mlsanity.com/paper/2009.11032/) (2020)
- [Learning to Fuse Sentences with Transformers for Summarization](https://mlsanity.com/paper/2010.03726/) (2020)

## Built on by (most cited first, 16 of 16)

- [RARR: Researching and Revising What Language Models Say, Using Language Models](https://mlsanity.com/paper/2210.08726/) (2022)
- [Conditional Generation with a Question-Answering Blueprint](https://mlsanity.com/paper/2207.00397/) (2022)
- [Life is a Circus and We are the Clowns: Automatically Finding Analogies between Situations and Processes](https://mlsanity.com/paper/2210.12197/) (2022)
- [A Question Answering Framework for Decontextualizing User-facing Snippets from Scientific Documents](https://mlsanity.com/paper/2305.14772/) (2023)
- [Shortcomings of Question Answering Based Factuality Frameworks for Error Localization](https://mlsanity.com/paper/2210.06748/) (2022)
- [QAPyramid: Fine-grained Evaluation of Content Selection for Text Summarization](https://mlsanity.com/paper/2412.07096/) (2024)
- [The Power of Summary-Source Alignments](https://mlsanity.com/paper/2406.00842/) (2024)
- [QA Is the New KR: Question-Answer Pairs as Knowledge Bases](https://mlsanity.com/paper/2207.00630/) (2022)
- [Revisiting Sentence Union Generation as a Testbed for Text Consolidation](https://mlsanity.com/paper/2305.15605/) (2023)
- [Unbalanced Optimal Transport for Unbalanced Word Alignment](https://mlsanity.com/paper/2306.04116/) (2023)
- [Incorporating Question Answering-Based Signals into Abstractive Summarization via Salient Span Selection](https://mlsanity.com/paper/2111.07935/) (2021)
- [Submodular Framework for Structured-Sparse Optimal Transport](https://mlsanity.com/paper/2406.04914/) (2024)
- [OMoS-QA: A Dataset for Cross-Lingual Extractive Question Answering in a German Migration Context](https://mlsanity.com/paper/2407.15736/) (2024)
- [QA-Noun: Representing Nominal Semantics via Natural Language Question-Answer Pairs](https://mlsanity.com/paper/2511.12504/) (2025)
- [Effective QA-driven Annotation of Predicate-Argument Relations Across Languages](https://mlsanity.com/paper/2602.22865/) (2026)
- [Overlap, Unique and Conflict: Can LLMs Extract What They Can Recognize?](https://mlsanity.com/paper/2609.38799/) (2026)

## Abstract (arXiv)

Multi-text applications, such as multi-document summarization, are typically required to model redundancies across related texts. Current methods confronting consolidation struggle to fuse overlapping information. In order to explicitly represent content overlap, we propose to align predicate-argument relations across texts, providing a potential scaffold for information consolidation. We go beyond clustering coreferring mentions, and instead model overlap with respect to redundancy at a propositional level, rather than merely detecting shared referents. Our setting exploits QA-SRL, utilizing question-answer pairs to capture predicate-argument relations, facilitating laymen annotation of cross-text alignments. We employ crowd-workers for constructing a dataset of QA-based alignments, and present a baseline QA alignment model trained over our dataset. Analyses show that our new task is semantically challenging, capturing content overlap beyond lexical similarity and complements cross-document coreference with proposition-level links, offering potential use for downstream tasks.