Multi-Document Summarization
93 papers with code • 5 benchmarks • 15 datasets
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
Benchmarks
These leaderboards are used to track progress in Multi-Document Summarization
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Datasets
Multi-News
WikiSum
WCEP
DUC 2004
aquamuse
OpoSum
MATINF
MS^2
Latest papers with no code
Understanding Position Bias Effects on Fairness in Social Multi-Document Summarization
Text summarization models have typically focused on optimizing aspects of quality such as fluency, relevance, and coherence, particularly in the context of news articles.
Multi-News+: Cost-efficient Dataset Cleansing via LLM-based Data Annotation
The quality of the dataset is crucial for ensuring optimal performance and reliability of downstream task models.
Attribute First, then Generate: Locally-attributable Grounded Text Generation
Recent efforts to address hallucinations in Large Language Models (LLMs) have focused on attributed text generation, which supplements generated texts with citations of supporting sources for post-generation fact-checking and corrections.
NewsQs: Multi-Source Question Generation for the Inquiring Mind
We present NewsQs (news-cues), a dataset that provides question-answer pairs for multiple news documents.
SKT5SciSumm - A Hybrid Generative Approach for Multi-Document Scientific Summarization
Summarization for scientific text has shown significant benefits both for the research community and human society.
Benchmarking LLMs on the Semantic Overlap Summarization Task
While recent advancements in Large Language Models (LLMs) have achieved superior performance in numerous summarization tasks, a benchmarking study of the SOS task using LLMs is yet to be performed.
Overview of the VLSP 2022 -- Abmusu Shared Task: A Data Challenge for Vietnamese Abstractive Multi-document Summarization
This paper reports the overview of the VLSP 2022 - Vietnamese abstractive multi-document summarization (Abmusu) shared task for Vietnamese News.
PELMS: Pre-training for Effective Low-Shot Multi-Document Summarization
We investigate pre-training techniques for abstractive multi-document summarization (MDS), which is much less studied than summarizing single documents.
Non-Parametric Memory Guidance for Multi-Document Summarization
Multi-document summarization (MDS) is a difficult task in Natural Language Processing, aiming to summarize information from several documents.
Mitigating Framing Bias with Polarity Minimization Loss
Framing bias plays a significant role in exacerbating political polarization by distorting the perception of actual events.
