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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

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Showing 51–75 of 359 papers

TitleStatusHype
Leveraging Long-Context Large Language Models for Multi-Document Understanding and Summarization in Enterprise Applications—0
BERT-VBD: Vietnamese Multi-Document Summarization Framework—0
GLIMMER: Incorporating Graph and Lexical Features in Unsupervised Multi-Document SummarizationCode0
SumRecom: A Personalized Summarization Approach by Learning from Users' Feedback—0
Rethinking Transformer-based Multi-document Summarization: An Empirical Investigation—0
LightPAL: Lightweight Passage Retrieval for Open Domain Multi-Document Summarization—0
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 Summarization—0
Multi-News+: Cost-efficient Dataset Cleansing via LLM-based Data AnnotationCode0
NewsQs: Multi-Source Question Generation for the Inquiring Mind—0
SKT5SciSumm -- Revisiting Extractive-Generative Approach for Multi-Document Scientific Summarization—0
Benchmarking LLMs on the Semantic Overlap Summarization Task—0
Shaping Political Discourse using multi-source News SummarizationCode0
OpenAsp: A Benchmark for Multi-document Open Aspect-based SummarizationCode0
Supervising the Centroid Baseline for Extractive Multi-Document SummarizationCode0
Overview of the VLSP 2022 -- Abmusu Shared Task: A Data Challenge for Vietnamese Abstractive Multi-document Summarization—0
PELMS: Pre-training for Effective Low-Shot Multi-Document SummarizationCode0
Non-Parametric Memory Guidance for Multi-Document SummarizationCode0
Mitigating Framing Bias with Polarity Minimization Loss—0
LLM Based Multi-Document Summarization Exploiting Main-Event Biased Monotone Submodular Content Extraction—0
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