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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
Parallel Hierarchical Transformer with Attention Alignment for Abstractive Multi-Document Summarization0
PeerSum: A Peer Review Dataset for Abstractive Multi-document Summarization0
PoBRL: Optimizing Multi-Document Summarization by Blending Reinforcement Learning Policies0
Predicting Intervention Approval in Clinical Trials through Multi-Document Summarization0
Predicting Salient Updates for Disaster Summarization0
Priberam Compressive Summarization Corpus: A New Multi-Document Summarization Corpus for European Portuguese0
Privacy-Preserving Multi-Document Summarization0
Probabilistic Domain Modelling With Contextualized Distributional Semantic Vectors0
Proceedings of the MultiLing 2013 Workshop on Multilingual Multi-document Summarization0
Query-Chain Focused Summarization0
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