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Multi-Document Summarization

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

Papers

Showing 2130 of 359 papers

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A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events PortalCode1
HowSumm: A Multi-Document Summarization Dataset Derived from WikiHow ArticlesCode1
MS2: Multi-Document Summarization of Medical StudiesCode1
Bottom-Up Abstractive SummarizationCode1
CAiRE-COVID: A Question Answering and Query-focused Multi-Document Summarization System for COVID-19 Scholarly Information ManagementCode1
Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple GranularitiesCode1
PeerSum: A Peer Review Dataset for Abstractive Multi-document SummarizationCode1
Pre-training via ParaphrasingCode1
PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document SummarizationCode1
SummPip: Unsupervised Multi-Document Summarization with Sentence Graph CompressionCode1
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