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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 331340 of 359 papers

TitleStatusHype
Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document SummarizationCode0
Hierarchical Transformers for Multi-Document SummarizationCode0
Automated Metrics for Medical Multi-Document Summarization Disagree with Human EvaluationsCode0
How "Multi" is Multi-Document Summarization?Code0
CQASUMM: Building References for Community Question Answering Summarization CorporaCode0
PELMS: Pre-training for Effective Low-Shot Multi-Document SummarizationCode0
Improving Fairness of Large Language Models in Multi-document SummarizationCode0
Which Information Matters? Dissecting Human-written Multi-document Summaries with Partial Information DecompositionCode0
Coverage-based Fairness in Multi-document SummarizationCode0
SgSum:Transforming Multi-document Summarization into Sub-graph SelectionCode0
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