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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 1120 of 359 papers

TitleStatusHype
PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document SummarizationCode1
MSˆ2: Multi-Document Summarization of Medical StudiesCode1
PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document SummarizationCode1
HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive SummarizationCode1
HowSumm: A Multi-Document Summarization Dataset Derived from WikiHow ArticlesCode1
SummerTime: Text Summarization Toolkit for Non-expertsCode1
TWAG: A Topic-Guided Wikipedia Abstract GeneratorCode1
Efficiently Summarizing Text and Graph Encodings of Multi-Document ClustersCode1
Transfer Learning for Sequence Generation: from Single-source to Multi-sourceCode1
MS2: Multi-Document Summarization of Medical StudiesCode1
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