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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 341–350 of 359 papers

TitleStatusHype
Extractive Multi-Document Summarization with Integer Linear Programming and Support Vector Regression—0
On the Effectiveness of using Sentence Compression Models for Query-Focused Multi-Document Summarization—0
Update Summarization Based on Co-Ranking with Constraints—0
A Supervised Aggregation Framework for Multi-Document Summarization—0
A Subjective Logic Framework for Multi-Document Summarization—0
RelationListwise for Query-Focused Multi-Document Summarization—0
Exploiting Category-Specific Information for Multi-Document Summarization—0
Thread Specific Features are Helpful for Identifying Subjectivity Orientation of Online Forum Threads—0
SentTopic-MultiRank: a Novel Ranking Model for Multi-Document Summarization—0
Framework of Automatic Text Summarization Using Reinforcement Learning—0
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