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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 231240 of 359 papers

TitleStatusHype
Towards Generating Personalized Hospitalization Summaries0
Towards Robust Abstractive Multi-Document Summarization: A Caseframe Analysis of Centrality and Domain0
Towards Syntax-aware Compositional Distributional Semantic Models0
UETrice at MEDIQA 2021: A Prosper-thy-neighbour Extractive Multi-document Summarization Model0
Understanding Position Bias Effects on Fairness in Social Multi-Document Summarization0
Unsupervised Aspect-Based Multi-Document Abstractive Summarization0
Unsupervised Multi-document Summarization with Holistic Inference0
Unsupervised Multi-document Summarization for News Corpus with Key Synonyms and Contextual Embeddings0
Unsupervised Summarization by Jointly Extracting Sentences and Keywords0
Update Summarization Based on Co-Ranking with Constraints0
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