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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 291300 of 359 papers

TitleStatusHype
Automatic Related Work Generation: A Meta Study0
BASS: Boosting Abstractive Summarization with Unified Semantic Graph0
Benchmarking LLMs on the Semantic Overlap Summarization Task0
BERT-VBD: Vietnamese Multi-Document Summarization Framework0
Beyond Centrality and Structural Features: Learning Information Importance for Text Summarization0
Can one size fit all?: Measuring Failure in Multi-Document Summarization Domain Transfer0
Cascaded Attention based Unsupervised Information Distillation for Compressive Summarization0
CIST System for CL-SciSumm 2016 Shared Task0
CIST System Report for ACL MultiLing 2013 -- Track 1: Multilingual Multi-document Summarization0
Coarse-to-Fine Query Focused Multi-Document Summarization0
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