A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal

Ghalandari, Demian Gholipour; Hokamp, Chris; Glover, J.; Ifrim, Georgiana

doi:10.18653/v1/2020.acl-main.120

Cited by 56 publications

(41 citation statements)

References 12 publications

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“…Wikipedia has also been used to construct datasets for other text generation tasks, such as generating Wikipedia movie plots (Orbach and Goldberg, 2020;Rashkin et al, 2020) and short Wikipedia event summaries (Gholipour Ghalandari et al, 2020), and summarizing Wikipedia documents (Zopf, 2018; or summaries of aspects of interests (Hayashi et al, 2020) from relevant documents.…”

Section: Related Workmentioning

confidence: 99%

WikiTableT: A Large-Scale Data-to-Text Dataset for Generating Wikipedia Article Sections

Chen

Wiseman

Gimpel

2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Datasets for data-to-text generation typically focus either on multi-domain, single-sentence generation or on single-domain, long-form generation. In this work, we cast generating Wikipedia sections as a data-to-text generation task and create a large-scale dataset, WIKITABLET, that pairs Wikipedia sections with their corresponding tabular data and various metadata. WIKITABLET contains millions of instances, covering a broad range of topics, as well as a variety of flavors of generation tasks with different levels of flexibility. We benchmark several training and decoding strategies on WIKITABLET. Our qualitative analysis shows that the best approaches can generate fluent and high quality texts but they struggle with coherence and factuality, showing the potential for our dataset to inspire future work on long-form generation. 1

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Section: Related Workmentioning

confidence: 99%

WikiTableT: A Large-Scale Data-to-Text Dataset for Generating Wikipedia Article Sections

Chen

Wiseman

Gimpel

2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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“…A small number of MDS datasets are available for other domains, including MultiNews (Fabbri et al, 2019), WikiSum , and Wikipedia Current Events (Gholipour Ghalandari et al, 2020). Most similar to MSˆ2 is MultiNews, where multiple news articles about the same event are summarized into one short paragraph.…”

Section: Related Workmentioning

confidence: 99%

MSˆ2: Multi-Document Summarization of Medical Studies

DeYoung¹,

Beltagy²,

Zuylen³

et al. 2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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To assess the effectiveness of any medical intervention, researchers must conduct a timeintensive and manual literature review. NLP systems can help to automate or assist in parts of this expensive process. In support of this goal, we release MSˆ2 (Multi-Document Summarization of Medical Studies), a dataset of over 470k documents and 20K summaries derived from the scientific literature. This dataset facilitates the development of systems that can assess and aggregate contradictory evidence across multiple studies, and is the first large-scale, publicly available multi-document summarization dataset in the biomedical domain. We experiment with a summarization system based on BART, with promising early results, though significant work remains to achieve higher summarization quality. We formulate our summarization inputs and targets in both free text and structured forms and modify a recently proposed metric to assess the quality of our system's generated summaries. Data and models are available at https:// github.com/allenai/ms2.

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“…It is also noted that several large-scale MDS datasets have been introduced in the news domain (Fabbri et al, 2019;Gu et al, 2020;Gholipour Ghalandari et al, 2020), for creating Wikipedia leadparagraphs (Liu et al, 2018), and for long-form question answering . However, these do not focus on the conversational domain.…”

Section: Dataset Statisticsmentioning

confidence: 99%

ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining

Fabbri¹,

Rahman²,

Rizvi³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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While online conversations can cover a vast amount of information in many different formats, abstractive text summarization has primarily focused on modeling solely news articles. This research gap is due, in part, to the lack of standardized datasets for summarizing online discussions. To address this gap, we design annotation protocols motivated by an issues-viewpoints-assertions framework to crowdsource four new datasets on diverse online conversation forms of news comments, discussion forums, community question answering forums, and email threads. We benchmark state-of-the-art models on our datasets and analyze characteristics associated with the data. To create a comprehensive benchmark, we also evaluate these models on widely-used conversation summarization datasets to establish strong baselines in this domain. Furthermore, we incorporate argument mining through graph construction to directly model the issues, viewpoints, and assertions present in a conversation and filter noisy input, showing comparable or improved results according to automatic and human evaluations.Headline: SuperBowl Snippet: Whether you're a football fan or not, what do you like about Super Bowl Sunday? Comment: ... In my opinion I think the Falcons will stomp the patriots. I think Tom Brady will choke the Super Bowl. ... Comment: I am big Arizona Cardinals fan so when they didn't even make the playoffs i was upset. ... Comment: I'm not a very big football fan at all. So when it comes to Superbowl Sunday, I'm in it for the commercials and the half time show. ...

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A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal

Cited by 56 publications

References 12 publications

WikiTableT: A Large-Scale Data-to-Text Dataset for Generating Wikipedia Article Sections

WikiTableT: A Large-Scale Data-to-Text Dataset for Generating Wikipedia Article Sections

MSˆ2: Multi-Document Summarization of Medical Studies

ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining

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