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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 271–280 of 359 papers

TitleStatusHype
Vector Space Models for Scientific Document Summarization—0
A Method of Accounting Bigrams in Topic Models—0
Topic Models: Accounting Component Structure of Bigrams—0
Improving Update Summarization via Supervised ILP and Sentence Reranking—0
Using External Resources and Joint Learning for Bigram Weighting in ILP-Based Multi-Document Summarization—0
Clustering Sentences with Density Peaks for Multi-document SummarizationCode0
Reader-Aware Multi-Document Summarization via Sparse Coding—0
Sentential Paraphrase Generation for Agglutinative Languages Using SVM with a String Kernel—0
Topic-based Multi-document Summarization using Differential Evolution forCombinatorial Optimization of Sentences—0
Multi-document Summarization Using Bipartite Graphs—0
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