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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
NewsQs: Multi-Source Question Generation for the Inquiring Mind0
SKT5SciSumm -- Revisiting Extractive-Generative Approach for Multi-Document Scientific Summarization0
Benchmarking LLMs on the Semantic Overlap Summarization Task0
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 Summarization0
PELMS: Pre-training for Effective Low-Shot Multi-Document SummarizationCode0
Non-Parametric Memory Guidance for Multi-Document SummarizationCode0
Mitigating Framing Bias with Polarity Minimization Loss0
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