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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 131140 of 359 papers

TitleStatusHype
Using Query Expansion in Manifold Ranking for Query-Oriented Multi-Document SummarizationCode0
Multi-Document Summarization withDeterminantal Point Process Attention0
AgreeSum: Agreement-Oriented Multi-Document Summarization0
Extending Multi-Document Summarization Evaluation to the Interactive SettingCode0
Nutri-bullets Hybrid: Consensual Multi-document Summarization0
UETrice at MEDIQA 2021: A Prosper-thy-neighbour Extractive Multi-document Summarization Model0
Neighborhood Rough Set based Multi-document Summarization0
BASS: Boosting Abstractive Summarization with Unified Semantic Graph0
Analysis of GraphSum's Attention Weights to Improve the Explainability of Multi-Document Summarization0
PoBRL: Optimizing Multi-Document Summarization by Blending Reinforcement Learning Policies0
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