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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 2130 of 359 papers

TitleStatusHype
LightPAL: Lightweight Passage Retrieval for Open Domain Multi-Document Summarization0
GLIMPSE: Pragmatically Informative Multi-Document Summarization for Scholarly ReviewsCode0
The Power of Summary-Source AlignmentsCode0
Text Generation: A Systematic Literature Review of Tasks, Evaluation, and ChallengesCode0
Which Information Matters? Dissecting Human-written Multi-document Summaries with Partial Information DecompositionCode0
Disentangling Specificity for Abstractive Multi-document SummarizationCode0
OARelatedWork: A Large-Scale Dataset of Related Work Sections with Full-texts from Open Access SourcesCode0
Understanding Position Bias Effects on Fairness in Social Multi-Document Summarization0
Multi-News+: Cost-efficient Dataset Cleansing via LLM-based Data AnnotationCode0
Attribute First, then Generate: Locally-attributable Grounded Text GenerationCode1
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