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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 7180 of 359 papers

TitleStatusHype
Overview of the VLSP 2022 -- Abmusu Shared Task: A Data Challenge for Vietnamese Abstractive Multi-document Summarization0
PELMS: Pre-training for Effective Low-Shot Multi-Document SummarizationCode0
Non-Parametric Memory Guidance for Multi-Document SummarizationCode0
Mitigating Framing Bias with Polarity Minimization Loss0
LLM Based Multi-Document Summarization Exploiting Main-Event Biased Monotone Submodular Content Extraction0
Controllable Multi-document Summarization: Coverage & Coherence Intuitive Policy with Large Language Model Based Rewards0
Multi-document Summarization: A Comparative Evaluation0
Unsupervised Multi-document Summarization with Holistic Inference0
Absformer: Transformer-based Model for Unsupervised Multi-Document Abstractive Summarization0
Pre-training Meets Clustering: A Hybrid Extractive Multi-document Summarization ModelCode0
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