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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 241250 of 359 papers

TitleStatusHype
Multi-document abstractive summarization using ILP based multi-sentence compression0
Beyond Centrality and Structural Features: Learning Information Importance for Text Summarization0
Optimizing an Approximation of ROUGE - a Problem-Reduction Approach to Extractive Multi-Document Summarization0
An Entity-Focused Approach to Generating Company Descriptions0
MDSWriter: Annotation Tool for Creating High-Quality Multi-Document Summarization CorporaCode0
Neural Sentence Ordering0
Int\'egration de la similarit\'e entre phrases comme crit\`ere pour le r\'esum\'e multi-document (Integrating sentence similarity as a constraint for multi-document summarization)0
A Sentence Compression Based Framework to Query-Focused Multi-Document Summarization0
Sort Story: Sorting Jumbled Images and Captions into Stories0
CIST System for CL-SciSumm 2016 Shared Task0
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