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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 311–320 of 359 papers

TitleStatusHype
Automatically Determining a Proper Length for Multi-Document Summarization: A Bayesian Nonparametric Approach—0
Summarizing Complex Events: a Cross-Modal Solution of Storylines Extraction and Reconstruction—0
Optimized Event Storyline Generation based on Mixture-Event-Aspect Model—0
Fast Joint Compression and Summarization via Graph Cuts—0
Multi-Document Summarization using Automatic Key-Phrase Extraction—0
Answering Questions from Multiple Documents -- the Role of Multi-Document Summarization—0
Knapsack Constrained Contextual Submodular List Prediction with Application to Multi-document Summarization—0
Proceedings of the MultiLing 2013 Workshop on Multilingual Multi-document Summarization—0
Using a Keyness Metric for Single and Multi Document Summarisation—0
The UWB Summariser at Multiling-2013—0
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