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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 131–140 of 359 papers

TitleStatusHype
CIST System for CL-SciSumm 2016 Shared Task—0
Ant Colony System for Multi-Document Summarization—0
Abstractive News Summarization based on Event Semantic Link Network—0
Hierarchical Summarization: Scaling Up Multi-Document Summarization—0
Cascaded Attention based Unsupervised Information Distillation for Compressive Summarization—0
From TimeLines to StoryLines: A preliminary proposal for evaluating narratives—0
Can one size fit all?: Measuring Failure in Multi-Document Summarization Domain Transfer—0
Answering Questions from Multiple Documents -- the Role of Multi-Document Summarization—0
Framework of Automatic Text Summarization Using Reinforcement Learning—0
Generating Supplementary Travel Guides from Social Media—0
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