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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

TitleStatusHype
Data Augmentation for Abstractive Query-Focused Multi-Document SummarizationCode1
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
Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement LearningCode1
Summary-Source Proposition-level Alignment: Task, Datasets and Supervised BaselineCode1
Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document SummarizationCode1
SummPip: Unsupervised Multi-Document Summarization with Sentence Graph CompressionCode1
Pre-training via ParaphrasingCode1
DynE: Dynamic Ensemble Decoding for Multi-Document SummarizationCode1
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