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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 311320 of 359 papers

TitleStatusHype
Fast Joint Compression and Summarization via Graph Cuts0
Summarizing Complex Events: a Cross-Modal Solution of Storylines Extraction and Reconstruction0
Automatically Determining a Proper Length for Multi-Document Summarization: A Bayesian Nonparametric Approach0
Using Shallow Semantic Parsing and Relation Extraction for Finding Contradiction in Text0
Multi-Document Summarization using Automatic Key-Phrase Extraction0
Answering Questions from Multiple Documents -- the Role of Multi-Document Summarization0
Knapsack Constrained Contextual Submodular List Prediction with Application to Multi-document Summarization0
ACL 2013 MultiLing Pilot Overview0
Multilingual summarization system based on analyzing the discourse structure at MultiLing 20130
Multilingual Summarization: Dimensionality Reduction and a Step Towards Optimal Term Coverage0
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