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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 281–290 of 359 papers

TitleStatusHype
Fear the REAPER: A System for Automatic Multi-Document Summarization with Reinforcement Learning—0
Improving Multi-documents Summarization by Sentence Compression based on Expanded Constituent Parse Trees—0
Automatic Generation of Related Work Sections in Scientific Papers: An Optimization Approach—0
Analyzing Stemming Approaches for Turkish Multi-Document Summarization—0
Exploiting Timegraphs in Temporal Relation Classification—0
Empirical analysis of exploiting review helpfulness for extractive summarization of online reviews—0
Towards Syntax-aware Compositional Distributional Semantic Models—0
Query-focused Multi-Document Summarization: Combining a Topic Model with Graph-based Semi-supervised Learning—0
Learning to Generate Coherent Summary with Discriminative Hidden Semi-Markov Model—0
Generating Supplementary Travel Guides from Social Media—0
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