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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

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Showing 241–250 of 359 papers

TitleStatusHype
Using a Keyness Metric for Single and Multi Document Summarisation—0
Using External Resources and Joint Learning for Bigram Weighting in ILP-Based Multi-Document Summarization—0
Using POMDPs for Topic-Focused Multi-Document Summarization (L'utilisation des POMDP pour les r\'esum\'es multi-documents orient\'es par une th\'ematique) [in French]—0
Using Shallow Semantic Parsing and Relation Extraction for Finding Contradiction in Text—0
Using Statistical and Semantic Models for Multi-Document Summarization—0
Utilizing Automatic Predicate-Argument Analysis for Concept Map Mining—0
Vector Space Models for Scientific Document Summarization—0
What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization—0
Tagging Funding Agencies and Grants in Scientific Articles using Sequential Learning Models—0
3M:Multi-document Summarization Considering Main and Minor Relationship—0
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