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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 141–150 of 359 papers

TitleStatusHype
Framework of Automatic Text Summarization Using Reinforcement Learning—0
Cascaded Attention based Unsupervised Information Distillation for Compressive Summarization—0
Highlight-Transformer: Leveraging Key Phrase Aware Attention to Improve Abstractive Multi-Document Summarization—0
Generating Related Work—0
Generating Supplementary Travel Guides from Social Media—0
CIST System for CL-SciSumm 2016 Shared Task—0
CIST System Report for ACL MultiLing 2013 -- Track 1: Multilingual Multi-document Summarization—0
Absformer: Transformer-based Model for Unsupervised Multi-Document Abstractive Summarization—0
An Unsupervised Multi-Document Summarization Framework Based on Neural Document Model—0
A Novel Feature-based Bayesian Model for Query Focused Multi-document Summarization—0
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