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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
Discriminative Marginalized Probabilistic Neural Method for Multi-Document Summarization of Medical Literature0
Document-aware Positional Encoding and Linguistic-guided Encoding for Abstractive Multi-document Summarization0
Do Multi-Document Summarization Models Synthesize?0
Drug Extraction from the Web: Summarizing Drug Experiences with Multi-Dimensional Topic Models0
DualSum: a Topic-Model based approach for update summarization0
Efficient Online Summarization of Microblogging Streams0
Empirical analysis of exploiting review helpfulness for extractive summarization of online reviews0
End-to-end Argument Generation System in Debating0
Entity-Aware Abstractive Multi-Document Summarization0
Entity-Supported Summarization of Biomedical Abstracts0
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