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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 181190 of 359 papers

TitleStatusHype
Hierarchical Transformers for Multi-Document SummarizationCode0
Solar Cell Surface Defect Inspection Based on Multispectral Convolutional Neural Network0
CQASUMM: Building References for Community Question Answering Summarization CorporaCode0
Adapting Neural Single-Document Summarization Model for Abstractive Multi-Document Summarization: A Pilot Study0
A Temporally Sensitive Submodularity Framework for Timeline SummarizationCode0
Deep Attentive Sentence Ordering Network0
基於基因演算法的組合式多文件摘要方法 (An Ensemble Approach for Multi-document Summarization using Genetic Algorithms) [In Chinese]0
Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document SummarizationCode0
Ant Colony System for Multi-Document Summarization0
Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion0
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