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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 181190 of 359 papers

TitleStatusHype
Utilizing Automatic Predicate-Argument Analysis for Concept Map Mining0
Vector Space Models for Scientific Document Summarization0
What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization0
Evaluating Pre-Trained Language Models on Multi-Document Summarization for Literature Reviews0
EventSum: A Large-Scale Event-Centric Summarization Dataset for Chinese Multi-News Documents0
Evolutionary Hierarchical Dirichlet Process for Timeline Summarization0
ExB Text Summarizer0
Exploiting Category-Specific Information for Multi-Document Summarization0
Exploiting Timegraphs in Temporal Relation Classification0
Exploiting Timelines to Enhance Multi-document Summarization0
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