# Multi-Document Keyphrase Extraction: Dataset, Baselines and Review

arXiv: https://arxiv.org/abs/2110.01073 · HTML: https://mlsanity.com/paper/2110.01073/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 53 references that are in the map.
- First posted on arXiv 3 Oct 2021.

## Builds on (most cited first, 12 of 12)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Unsupervised Keyphrase Extraction with Multipartite Graphs](https://mlsanity.com/paper/1803.08721/) (2018)
- [Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization](https://mlsanity.com/paper/1808.06218/) (2018)
- [A Review of Keyphrase Extraction](https://mlsanity.com/paper/1905.05044/) (2019)
- [Cross-document Coreference Resolution over Predicted Mentions](https://mlsanity.com/paper/2106.01210/) (2021)
- [Open Domain Web Keyphrase Extraction Beyond Language Modeling](https://mlsanity.com/paper/1911.02671/) (2019)
- [Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learning](https://mlsanity.com/paper/2010.00117/) (2020)
- [Capturing Global Informativeness in Open Domain Keyphrase Extraction](https://mlsanity.com/paper/2004.13639/) (2020)
- [Supervised Topical Key Phrase Extraction of News Stories using Crowdsourcing, Light Filtering and Co-reference Normalization](https://mlsanity.com/paper/1306.4886/) (2013)
- [Extract with Order for Coherent Multi-Document Summarization](https://mlsanity.com/paper/1706.06542/) (2017)
- [Rookie: A unique approach for exploring news archives](https://mlsanity.com/paper/1708.01944/) (2017)
- [Unsupervised Summarization by Jointly Extracting Sentences and Keywords](https://mlsanity.com/paper/2009.07481/) (2020)

## 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.