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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 201–210 of 359 papers

TitleStatusHype
Extractive Summarization using Continuous Vector Space Models—0
Extract with Order for Coherent Multi-Document Summarization—0
Fast and Robust Compressive Summarization with Dual Decomposition and Multi-Task Learning—0
Fast Joint Compression and Summarization via Graph Cuts—0
Fear the REAPER: A System for Automatic Multi-Document Summarization with Reinforcement Learning—0
Framework of Automatic Text Summarization Using Reinforcement Learning—0
From TimeLines to StoryLines: A preliminary proposal for evaluating narratives—0
Generating an Overview Report over Many Documents—0
Generating Related Work—0
Generating Supplementary Travel Guides from Social Media—0
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