We propose the task of updated headline generation, in which a system generates a headline for an updated article, considering both the previous article and headline. The system must identify the novel information in the article update, and modify the existing headline accordingly. We create data for this task using the NewsEdits corpus (Spangher and May, 2021) by automatically identifying contiguous article versions that are likely to require a substantive headline update. We find that models conditioned on the prior headline and body re-visions produce headlines judged by humans to be as factual as gold headlines while making fewer unnecessary edits compared to a standard headline generation model. Our experiments establish benchmarks for this new contextual summarization task.

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In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, May 2022.

@inproceedings{Panthaplackel:acl2022updated, title={Updated Headline Generation: Creating Updated Summaries for Evolving News Stories}, author={Sheena Panthaplackel and Adrian Benton and Mark Dredze}, booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics}, month={May}, url="http://www.cs.utexas.edu/users/ai-labpub-view.php?PubID=127959", year={2022} }

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