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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 71–80 of 359 papers

TitleStatusHype
A Hybrid Approach to Multi-document Summarization of Opinions in Reviews—0
Concept-Map-Based Multi-Document Summarization using Concept Coreference Resolution and Global Importance Optimization—0
Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion—0
Abstractive Multi-document Summarization by Partial Tree Extraction, Recombination and Linearization—0
Do Multi-Document Summarization Models Synthesize?—0
A Preliminary Study of Tweet Summarization using Information Extraction—0
Complex Question Answering: Unsupervised Learning Approaches and Experiments—0
AgreeSum: Agreement-Oriented Multi-Document Summarization—0
Combining State-of-the-Art Models with Maximal Marginal Relevance for Few-Shot and Zero-Shot Multi-Document Summarization—0
Coarse-to-Fine Query Focused Multi-Document Summarization—0
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