Multi-Document Summarization
93 papers with code • 5 benchmarks • 15 datasets
Multi-Document Summarization is a process of representing a set of documents with a short piece of text by capturing the relevant information and filtering out the redundant information. Two prominent approaches to Multi-Document Summarization are extractive and abstractive summarization. Extractive summarization systems aim to extract salient snippets, sentences or passages from documents, while abstractive summarization systems aim to concisely paraphrase the content of the documents.
Source: Multi-Document Summarization using Distributed Bag-of-Words Model
Benchmarks
These leaderboards are used to track progress in Multi-Document Summarization
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Datasets
Multi-News
WikiSum
WCEP
DUC 2004
aquamuse
OpoSum
MATINF
MS^2
Latest papers
Shaping Political Discourse using multi-source News Summarization
c-rajan/Multi-source-NewsSummarization
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Multi-document summarization is the process of automatically generating a concise summary of multiple documents related to the same topic.
OpenAsp: A Benchmark for Multi-document Open Aspect-based Summarization
To advance research on more realistic scenarios, we introduce OpenAsp, a benchmark for multi-document \textit{open} aspect-based summarization.
Supervising the Centroid Baseline for Extractive Multi-Document Summarization
The centroid method is a simple approach for extractive multi-document summarization and many improvements to its pipeline have been proposed.
Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News Articles
In this paper, we propose a new task of summarizing diverse information encountered in multiple news articles encompassing the same event.
ODSum: New Benchmarks for Open Domain Multi-Document Summarization
Open-domain Multi-Document Summarization (ODMDS) is a critical tool for condensing vast arrays of documents into coherent, concise summaries.
Pre-training Meets Clustering: A Hybrid Extractive Multi-document Summarization Model
Outcomes validate that our proposed model shows greatly enhanced performance as compared to the existent unsupervised state-of-the-art approaches.
Revisiting Sentence Union Generation as a Testbed for Text Consolidation
In this paper, we suggest revisiting the sentence union generation task as an effective well-defined testbed for assessing text consolidation capabilities, decoupling the consolidation challenge from subjective content selection.
Automated Metrics for Medical Multi-Document Summarization Disagree with Human Evaluations
We analyze how automated summarization evaluation metrics correlate with lexical features of generated summaries, to other automated metrics including several we propose in this work, and to aspects of human-assessed summary quality.
A Hierarchical Encoding-Decoding Scheme for Abstractive Multi-document Summarization
Pre-trained language models (PLMs) have achieved outstanding achievements in abstractive single-document summarization (SDS).
Summarizing Multiple Documents with Conversational Structure for Meta-Review Generation
oaimli/peersum
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We present PeerSum, a novel dataset for generating meta-reviews of scientific papers.
