DIFFQG: Generating Questions to Summarize Factual Changes
Not on the map. Why?
Abstract (arXiv)
Identifying the difference between two versions of the same article is useful to update knowledge bases and to understand how articles evolve. Paired texts occur naturally in diverse situations: reporters write similar news stories and maintainers of authoritative websites must keep their information up to date. We propose representing factual changes between paired documents as question-answer pairs, where the answer to the same question differs between two versions. We find that question-answer pairs can flexibly and concisely capture the updated contents. Provided with paired documents, annotators identify questions that are answered by one passage but answered differently or cannot be answered by the other. We release DIFFQG which consists of 759 QA pairs and 1153 examples of paired passages with no factual change. These questions are intended to be both unambiguous and information-seeking and involve complex edits, pushing beyond the capabilities of current question generation and factual change detection systems. Our dataset summarizes the changes between two versions of the document as questions and answers, studying automatic update summarization in a novel way.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 19 of its 28 references that are in the map. First posted on arXiv 1 Mar 2023.
Builds on
19 of 28 references.
References · 19
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
- 2015A large annotated corpus for learning natural language inferencecited by 1,847
- 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,554
- 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,375
- 2021Time-Aware Language Models as Temporal Knowledge Basescited by 296
- 2021Get Your Vitamin C! Robust Fact Verification with Contrastive Evidencecited by 225
- 2021PAQ: 65 Million Probably-Asked Questions and What You Can Do With Themcited by 200
- 2018Constituency Parsing with a Self-Attentive Encodercited by 199
- 2021Decontextualization: Making Sentences Stand-Alonecited by 102
7 more
- 2020NILE : Natural Language Inference with Faithful Natural Language Explanationscited by 96
- 2022PEER: A Collaborative Language Modelcited by 86
- 2022Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluationcited by 67
- 2021FRUIT: Faithfully Reflecting Updated Information in Textcited by 39
- 2018WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discoursecited by 26
- 2020Learning to Evaluate Translation Beyond English: BLEURT Submissions to the WMT Metrics 2020 Shared Taskcited by 25
- 2021Asking It All: Generating Contextualized Questions for any Semantic Rolecited by 22
Built on by
Citing papers per year: 2023: 1; 2024: 2.
3 papers.