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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

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Showing 171–180 of 359 papers

TitleStatusHype
Unsupervised Aspect-Based Multi-Document Abstractive Summarization—0
Unsupervised Multi-document Summarization with Holistic Inference—0
Unsupervised Multi-document Summarization for News Corpus with Key Synonyms and Contextual Embeddings—0
Unsupervised Summarization by Jointly Extracting Sentences and Keywords—0
Update Summarization Based on Co-Ranking with Constraints—0
Using a Keyness Metric for Single and Multi Document Summarisation—0
Using External Resources and Joint Learning for Bigram Weighting in ILP-Based Multi-Document Summarization—0
Using POMDPs for Topic-Focused Multi-Document Summarization (L'utilisation des POMDP pour les r\'esum\'es multi-documents orient\'es par une th\'ematique) [in French]—0
Using Shallow Semantic Parsing and Relation Extraction for Finding Contradiction in Text—0
Using Statistical and Semantic Models for Multi-Document Summarization—0
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