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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 311320 of 359 papers

TitleStatusHype
Clustering Sentences with Density Peaks for Multi-document SummarizationCode0
OpenAsp: A Benchmark for Multi-document Open Aspect-based SummarizationCode0
Flight of the PEGASUS? Comparing Transformers on Few-shot and Zero-shot Multi-document Abstractive SummarizationCode0
A Hierarchical Encoding-Decoding Scheme for Abstractive Multi-document SummarizationCode0
From Single to Multi: How LLMs Hallucinate in Multi-Document SummarizationCode0
A General Optimization Framework for Multi-Document Summarization Using Genetic Algorithms and Swarm IntelligenceCode0
ELSKE: Efficient Large-Scale Keyphrase ExtractionCode0
Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document InputsCode0
Generating a Structured Summary of Numerous Academic Papers: Dataset and MethodCode0
Disentangling Specificity for Abstractive Multi-document SummarizationCode0
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