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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 151160 of 359 papers

TitleStatusHype
Drug Extraction from the Web: Summarizing Drug Experiences with Multi-Dimensional Topic Models0
Do Multi-Document Summarization Models Synthesize?0
Assessing the performance of Olelo, a real-time biomedical question answering application0
A Method of Accounting Bigrams in Topic Models0
ACL 2013 MultiLing Pilot Overview0
Abstractive Multi-Document Summarization via Phrase Selection and Merging0
Document-aware Positional Encoding and Linguistic-guided Encoding for Abstractive Multi-document Summarization0
Discriminative Marginalized Probabilistic Neural Method for Multi-Document Summarization of Medical Literature0
Detection of Topic and its Extrinsic Evaluation Through Multi-Document Summarization0
A Spectral Method for Unsupervised Multi-Document Summarization0
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