DIFFQG: Generating Questions to Summarize Factual Changes
This addresses the need for automatic update summarization in knowledge base maintenance and article evolution analysis, though it is incremental as it builds on existing question generation and change detection methods.
The authors tackled the problem of summarizing factual changes between two versions of a document by representing them as question-answer pairs, where answers differ between versions, and released a dataset of 759 QA pairs and 1153 examples to support this approach.
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.