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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 151160 of 359 papers

TitleStatusHype
Multi-document Summarization via Deep Learning Techniques: A Survey0
WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive SummarizationCode0
Topic-Centric Unsupervised Multi-Document Summarization of Scientific and News Articles0
Abstractive Multi-Document Summarization via Joint Learning with Single-Document SummarizationCode0
A Spectral Method for Unsupervised Multi-Document Summarization0
Coarse-to-Fine Query Focused Multi-Document Summarization0
SupMMD: A Sentence Importance Model for Extractive Summarization using Maximum Mean DiscrepancyCode0
Corpora Evaluation and System Bias Detection in Multi-document SummarizationCode0
Unsupervised Summarization by Jointly Extracting Sentences and Keywords0
Global-aware Beam Search for Neural Abstractive SummarizationCode0
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