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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 231240 of 359 papers

TitleStatusHype
A Redundancy-Aware Sentence Regression Framework for Extractive Summarization0
Bridging the gap between extractive and abstractive summaries: Creation and evaluation of coherent extracts from heterogeneous sourcesCode0
An Unsupervised Multi-Document Summarization Framework Based on Neural Document Model0
Revisiting the Evaluation for Cross Document Event Coreference0
A General Optimization Framework for Multi-Document Summarization Using Genetic Algorithms and Swarm IntelligenceCode0
The Next Step for Multi-Document Summarization: A Heterogeneous Multi-Genre Corpus Built with a Novel Construction ApproachCode0
Abstractive News Summarization based on Event Semantic Link Network0
Entity-Supported Summarization of Biomedical Abstracts0
Improving Multi-Document Summarization via Text Classification0
News Stream Summarization using Burst Information Networks0
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