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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 301310 of 359 papers

TitleStatusHype
Extracting News Web Page Creation Time with DCTFinder0
LQVSumm: A Corpus of Linguistic Quality Violations in Multi-Document Summarization0
A Repository of State of the Art and Competitive Baseline Summaries for Generic News Summarization0
Extractive Summarization using Continuous Vector Space Models0
Improving the Estimation of Word Importance for News Multi-Document Summarization0
Efficient Online Summarization of Microblogging Streams0
Complex Question Answering: Unsupervised Learning Approaches and Experiments0
Multi-Topic Multi-Document Summarizer0
Mining the Gaps: Towards Polynomial Summarization0
Using Shallow Semantic Parsing and Relation Extraction for Finding Contradiction in Text0
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