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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 141150 of 359 papers

TitleStatusHype
A Spectral Method for Unsupervised Multi-Document Summarization0
Coarse-to-Fine Query Focused Multi-Document Summarization0
Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific ArticlesCode1
AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document SummarizationCode1
Quantitative Argument Summarization and Beyond: Cross-Domain Key Point AnalysisCode1
SupMMD: A Sentence Importance Model for Extractive Summarization using Maximum Mean DiscrepancyCode0
Corpora Evaluation and System Bias Detection in Multi-document SummarizationCode0
Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement LearningCode1
Unsupervised Summarization by Jointly Extracting Sentences and Keywords0
Global-aware Beam Search for Neural Abstractive SummarizationCode0
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