WebAQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using … WebAQuaMuSe (Kulkarni et al.,2024) is a query-focused multi-document summarization dataset with user-written queries and human-verified long-answer summaries from the Natural …
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Web23 ott 2024 · AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization. Summarization is the task of compressing source document … Web27 ott 2024 · It is an important technique that can be beneficial to a variety of applications such as search engines, document-level machine reading comprehension, and chatbots. Currently, datasets designed for query-based summarization are short in numbers and existing datasets are also limited in both scale and quality. islehopper set sea of thieves
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Web80 papers with code • 5 benchmarks • 14 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. WebOASum is a large-scale open-domain aspect-based summarization dataset which contains more than 3.7 million instances with around 1 million different aspects on 2 million Wikipedia pages. WebCTE: A Dataset for Contextualized Table Extraction [1.1859913430860336] The dataset comprises 75k fully annotated pages of scientific papers, including more than 35k tables. Data are gathered from PubMed Central, merging the information provided by annotations in the PubTables-1M and PubLayNet datasets. kfc in patterson