Pith. sign in

REVIEW 2 cited by

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2005.10070 v1 pith:DTBKHQTC submitted 2020-05-20 cs.CL

Demian Gholipour Ghalandari , Chris Hokamp , Nghia The Pham , John Glover , Georgiana Ifrim This is my paper
classification cs.CL
keywords datasetarticleseventslargeclusterscurrentdocumentmulti-document
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original 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.

Discussion (0). Sign in to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. WETBench: A Benchmark for Detecting Task-Specific Machine-Generated Text on Wikipedia

    cs.CL 2025-07 conditional novelty 6.0 of 10

    WETBench shows that existing machine-generated text detectors, particularly zero-shot methods, underperform on task-specific Wikipedia editing scenarios, with supervised detectors averaging 78% accuracy and zero-shot ...

  2. Multi-Agent Interactive Question Generation Framework for Long Document Understanding

    cs.CL 2025-07 conditional novelty 4.0 of 10

    A multi-agent question generation pipeline produces long-context English and Arabic QA pairs (AraEngLongBench), and top LVLMs score below 50% on the resulting benchmark.

Pith tools