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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 251260 of 359 papers

TitleStatusHype
Absformer: Transformer-based Model for Unsupervised Multi-Document Abstractive Summarization0
Abstractive Multi-document Summarization by Partial Tree Extraction, Recombination and Linearization0
Abstractive Multi-Document Summarization via Phrase Selection and Merging0
Abstractive Multi-document Summarization with Semantic Information Extraction0
Abstractive News Summarization based on Event Semantic Link Network0
Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion0
Abstract Meaning Representation for Multi-Document Summarization0
ACL 2013 MultiLing Pilot Overview0
ACM -- Attribute Conditioning for Abstractive Multi Document Summarization0
Adapting Neural Single-Document Summarization Model for Abstractive Multi-Document Summarization: A Pilot Study0
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