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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 141150 of 359 papers

TitleStatusHype
Error Analysis of using BART for Multi-Document Summarization: A Study for English and German LanguageCode0
Identifying Helpful Sentences in Product Reviews0
Generating Related Work0
What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization0
Nutri-bullets: Summarizing Health Studies by Composing SegmentsCode0
Multi-document Summarization using Semantic Role Labeling and Semantic Graph for Indonesian News Article0
ELSKE: Efficient Large-Scale Keyphrase ExtractionCode0
A novel extractive multi-document text summarization system using quantum-inspired genetic algorithm: MTSQIGA0
Flight of the PEGASUS? Comparing Transformers on Few-shot and Zero-shot Multi-document Abstractive SummarizationCode0
Neural Abstractive Multi-Document Summarization: Hierarchical or Flat Structure?0
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