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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 121130 of 359 papers

TitleStatusHype
Extending Multi-Text Sentence Fusion Resources via Pyramid AnnotationsCode0
Unsupervised Multi-document Summarization for News Corpus with Key Synonyms and Contextual Embeddings0
Leveraging Attribute Conditioning for Abstractive Multi Document Summarization0
QA-Align: Representing Cross-Text Content Overlap by Aligning Question-Answer PropositionsCode0
Dependency Structure for News Document Summarization0
MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News SummarizationCode0
Monolingual versus Multilingual BERTology for Vietnamese Extractive Multi-Document Summarization0
Entity-Aware Abstractive Multi-Document Summarization0
Highlight-Transformer: Leveraging Key Phrase Aware Attention to Improve Abstractive Multi-Document Summarization0
DESCGEN: A Distantly Supervised Datasetfor Generating Entity DescriptionsCode0
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