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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 3140 of 359 papers

TitleStatusHype
AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document SummarizationCode1
Attribute First, then Generate: Locally-attributable Grounded Text GenerationCode1
GameWikiSum: a Novel Large Multi-Document Summarization DatasetCode1
Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document SummarizationCode1
Improving Multi-Document Summarization through Referenced Flexible Extraction with Credit-AwarenessCode1
Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document SummarizationCode0
Error Analysis of using BART for Multi-Document Summarization: A Study for English and German LanguageCode0
Abstractive Multi-Document Summarization via Joint Learning with Single-Document SummarizationCode0
ELSKE: Efficient Large-Scale Keyphrase ExtractionCode0
Estimating Optimal Context Length for Hybrid Retrieval-augmented Multi-document SummarizationCode0
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