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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 281290 of 359 papers

TitleStatusHype
News Stream Summarization using Burst Information Networks0
Nutri-bullets Hybrid: Consensual Multi-document Summarization0
On-Demand Distributional Semantic Distance and Paraphrasing0
Error Analysis of using BART for Multi-Document Summarization: A Study for English and German LanguageCode0
Estimating Optimal Context Length for Hybrid Retrieval-augmented Multi-document SummarizationCode0
Leveraging Graph to Improve Abstractive Multi-Document SummarizationCode0
The Next Step for Multi-Document Summarization: A Heterogeneous Multi-Genre Corpus Built with a Novel Construction ApproachCode0
The Power of Summary-Source AlignmentsCode0
UPER: Boosting Multi-Document Summarization with an Unsupervised Prompt-based ExtractorCode0
WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive SummarizationCode0
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