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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 171180 of 359 papers

TitleStatusHype
Subtopic-driven Multi-Document Summarization0
Multi-Document Summarization with Determinantal Point Processes and Contextualized Representations0
Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document InputsCode0
Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization0
Generating an Overview Report over Many Documents0
Joint Lifelong Topic Model and Manifold Ranking for Document Summarization0
Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical ModelCode0
Fast Concept Mention Grouping for Concept Map-based Multi-Document SummarizationCode0
Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document SummarizationCode0
Scoring Sentence Singletons and Pairs for Abstractive SummarizationCode0
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