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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 191200 of 359 papers

TitleStatusHype
The SUMMA Platform: A Scalable Infrastructure for Multi-lingual Multi-media Monitoring0
Learning Thematic Similarity Metric from Article Sections Using Triplet Networks0
Abstract Meaning Representation for Multi-Document Summarization0
Towards Generating Personalized Hospitalization Summaries0
Using Statistical and Semantic Models for Multi-Document Summarization0
Auto-hMDS: Automatic Construction of a Large Heterogeneous Multilingual Multi-Document Summarization CorpusCode0
Beyond Generic Summarization: A Multi-faceted Hierarchical Summarization Corpus of Large Heterogeneous DataCode0
A Repository of Corpora for Summarization0
Towards a Neural Network Approach to Abstractive Multi-Document Summarization0
Content based Weighted Consensus Summarization0
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