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

Abstract

Multi-document summarization (MDS) aims to compress the content in large document collections into short summaries and has important applications in story clustering for newsfeeds, presentation of search results, and timeline generation. However, there is a lack of datasets that realistically address such use cases at a scale large enough for training supervised models for this task. This work presents a new dataset for MDS that is large both in the total number of document clusters and in the size of individual clusters. We build this dataset by leveraging the Wikipedia Current Events Portal (WCEP), which provides concise and neutral human-written summaries of news events, with links to external source articles. We also automatically extend these source articles by looking for related articles in the Common Crawl archive. We provide a quantitative analysis of the dataset and empirical results for several state-of-the-art MDS techniques. The dataset is available at https://github.com/complementizer/ wcep-mds-dataset.

Chain-of-event prompting for multi-document summarization by large language models

“…Supervised methods in abstractive summarization always use the encoder-decoder transformer architecture with data sets of large, paired document-summary examples. Ghalandari et al (2020) propose an end-to-end Hierarchical MMR-Attention Pointergenerator (Hi-MAP) model to address the information redundancy. Li et al (2020) develop a neural abstractive MDS model which can leverage similarity graph or discourse graph representations of documents, to more effectively capture cross-document relations.…”

“…WCEP (Ghalandari et al, 2020). WCEP data set contains human-written summaries of recent news events.…”