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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 331–340 of 359 papers

TitleStatusHype
Evolutionary Hierarchical Dirichlet Process for Timeline Summarization—0
Towards Robust Abstractive Multi-Document Summarization: A Caseframe Analysis of Centrality and Domain—0
Probabilistic Domain Modelling With Contextualized Distributional Semantic Vectors—0
Fast and Robust Compressive Summarization with Dual Decomposition and Multi-Task Learning—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
A Preliminary Study of Tweet Summarization using Information Extraction—0
Drug Extraction from the Web: Summarizing Drug Experiences with Multi-Dimensional Topic Models—0
Towards Coherent Multi-Document Summarization—0
A Novel Feature-based Bayesian Model for Query Focused Multi-document Summarization—0
Query-focused Multi-document Summarization: Combining a Novel Topic Model with Graph-based Semi-supervised Learning—0
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