Multi-Document Keyphrase Extraction: Dataset, Baselines and Review
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Abstract (arXiv)
Keyphrase extraction has been extensively researched within the single-document setting, with an abundance of methods, datasets and applications. In contrast, multi-document keyphrase extraction has been infrequently studied, despite its utility for describing sets of documents, and its use in summarization. Moreover, no prior dataset exists for multi-document keyphrase extraction, hindering the progress of the task. Recent advances in multi-text processing make the task an even more appealing challenge to pursue. To stimulate this pursuit, we present here the first dataset for the task, MK-DUC-01, which can serve as a new benchmark, and test multiple keyphrase extraction baselines on our data. In addition, we provide a brief, yet comprehensive, literature review of the task.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 53 references that are in the map. First posted on arXiv 3 Oct 2021.
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References · 12
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2018Unsupervised Keyphrase Extraction with Multipartite Graphscited by 50
- 2018Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarizationcited by 42
- 2019A Review of Keyphrase Extractioncited by 33
- 2021Cross-document Coreference Resolution over Predicted Mentionscited by 29
- 2019Open Domain Web Keyphrase Extraction Beyond Language Modelingcited by 28
- 2020Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learningcited by 28
- 2020Capturing Global Informativeness in Open Domain Keyphrase Extractioncited by 17
- 2013Supervised Topical Key Phrase Extraction of News Stories using Crowdsourcing, Light Filtering and Co-reference Normalizationcited by 10
- 2017Extract with Order for Coherent Multi-Document Summarizationcited by 9
- 2017Rookie: A unique approach for exploring news archivescited by 1
- 2020Unsupervised Summarization by Jointly Extracting Sentences and Keywordscited by 1
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