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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 8190 of 359 papers

TitleStatusHype
Predicting Intervention Approval in Clinical Trials through Multi-Document Summarization0
A Multi-Document Coverage Reward for RELAXed Multi-Document SummarizationCode0
PeerSum: A Peer Review Dataset for Abstractive Multi-document SummarizationCode1
Proposition-Level Clustering for Multi-Document SummarizationCode1
Automatic Related Work Generation: A Meta Study0
Proposition-Level Clustering for Multi-Document SummarizationCode1
LongT5: Efficient Text-To-Text Transformer for Long SequencesCode1
PeerSum: A Peer Review Dataset for Abstractive Multi-document Summarization0
PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document SummarizationCode1
A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization0
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