NEWTS: A Corpus for News Topic-Focused Summarization
Seyed Ali Bahrainian, Sheridan Feucht, Carsten Eickhoff
2022Findings of the Association for Computational Linguistics: ACL 202213 citationsDOIOpen Access PDF
Abstract
Text summarization models are approaching human levels of fidelity. Existing benchmarking corpora provide concordant pairs of full and abridged versions of Web, news or, professional content. To date, all summarization datasets operate under a one-size-fits-all paradigm that may not reflect the full range of organic summarization needs. Several recently proposed models (e.g., plug and play language models) have the capacity to condition the generated summaries on a desired range of themes. These capacities remain largely unused and unevaluated as there is no dedicated dataset that would support the task of topic-focused summarization.
Topics & Concepts
Automatic summarizationComputer scienceBenchmarkingInformation retrievalTask (project management)Multi-document summarizationRange (aeronautics)FidelityNatural language processingTheme (computing)Artificial intelligenceWorld Wide WebComposite materialEconomicsTelecommunicationsManagementMarketingBusinessMaterials scienceTopic ModelingNatural Language Processing TechniquesAdvanced Text Analysis Techniques