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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

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Showing 231–240 of 359 papers

TitleStatusHype
LBMT team at VLSP2022-Abmusu: Hybrid method with text correlation and generative models for Vietnamese multi-document summarization—0
Learning Thematic Similarity Metric from Article Sections Using Triplet Networks—0
Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization—0
Learning to Generate Coherent Summary with Discriminative Hidden Semi-Markov Model—0
Learning to Score System Summaries for Better Content Selection Evaluation.—0
Leveraging Attribute Conditioning for Abstractive Multi Document Summarization—0
Leveraging Long-Context Large Language Models for Multi-Document Understanding and Summarization in Enterprise Applications—0
LightPAL: Lightweight Passage Retrieval for Open Domain Multi-Document Summarization—0
LLM Based Multi-Document Summarization Exploiting Main-Event Biased Monotone Submodular Content Extraction—0
LM Agents for Coordinating Multi-User Information Gathering—0
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