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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

Showing 281290 of 359 papers

TitleStatusHype
A Repository of State of the Art and Competitive Baseline Summaries for Generic News Summarization0
A Sentence Compression Based Framework to Query-Focused Multi-Document Summarization0
A Spectral Method for Unsupervised Multi-Document Summarization0
Assessing the performance of Olelo, a real-time biomedical question answering application0
A Subjective Logic Framework for Multi-Document Summarization0
A Supervised Aggregation Framework for Multi-Document Summarization0
A Unified Retrieval Framework with Document Ranking and EDU Filtering for Multi-document Summarization0
Automatically Determining a Proper Length for Multi-Document Summarization: A Bayesian Nonparametric Approach0
Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challenges0
Automatic Generation of Related Work Sections in Scientific Papers: An Optimization Approach0
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