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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 101110 of 359 papers

TitleStatusHype
ELSKE: Efficient Large-Scale Keyphrase ExtractionCode0
Extending Multi-Document Summarization Evaluation to the Interactive SettingCode0
Global-aware Beam Search for Neural Abstractive SummarizationCode0
A Multi-Document Coverage Reward for RELAXed Multi-Document SummarizationCode0
Extending Multi-Text Sentence Fusion Resources via Pyramid AnnotationsCode0
Revisiting Sentence Union Generation as a Testbed for Text ConsolidationCode0
Improving Fairness of Large Language Models in Multi-document SummarizationCode0
Estimating Optimal Context Length for Hybrid Retrieval-augmented Multi-document SummarizationCode0
PDSum: Prototype-driven Continuous Summarization of Evolving Multi-document Sets StreamCode0
WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive SummarizationCode0
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