NEWTS: A Corpus for News Topic-Focused Summarization

Bahrainian, Seyed Ali; Feucht, Sheridan; Eickhoff, Carsten

doi:10.18653/v1/2022.findings-acl.42

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…ProphetNet (Qi et al, 2020) is a supervised abstractive summarization model that predicts the next n tokens simultaneously. The results for ProphetNet are taken from the NEWTS paper (Bahrainian et al, 2022).…”

Section: Baselinesmentioning

confidence: 99%

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LMGQS: A Large-scale Dataset for Query-focused Summarization

Xu,

Wang,

Liu

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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Query-focused summarization (QFS) aims to extract or generate a summary of an input document that directly answers or is relevant to a given query. The lack of large-scale datasets in the form of documents, queries, and summaries has hindered model development in this area. In contrast, multiple large-scale highquality datasets for generic summarization exist. We hypothesize that there is a hidden query for each summary sentence in a generic summarization annotation, and we utilize a largescale pretrained language model to recover it. In this way, we convert four generic summarization benchmarks into a new QFS benchmark dataset, LMGQS, which consists of over 1 million document-query-summary samples. We thoroughly investigate the properties of our proposed dataset and establish baselines with state-of-the-art summarization models. By fine-tuning a language model on LMGQS, we achieve state-of-the-art zero-shot and supervised performance on multiple existing QFS benchmarks, demonstrating the high quality and diversity of LMGQS.

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Section: Baselinesmentioning

confidence: 99%

“…In NEWTS, the topic query is represented in three formats: word, phrase, and sentence. All baseline results except for InstructGPT 002 are from(Bahrainian et al, 2022). InstructGPT 002 takes topic words as query, which performs the best among all topic formats.…”

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confidence: 99%