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pro vyhledávání: '"Ghalandari, Demian Gholipour"'
Autor:
Boylan, Jack, Mangla, Shashank, Thorn, Dominic, Ghalandari, Demian Gholipour, Ghaffari, Parsa, Hokamp, Chris
This study explores the use of Large Language Models (LLMs) for automatic evaluation of knowledge graph (KG) completion models. Historically, validating information in KGs has been a challenging task, requiring large-scale human annotation at prohibi
Externí odkaz:
http://arxiv.org/abs/2404.15923
We present an open-source Python library for building and using datasets where inputs are clusters of textual data, and outputs are sequences of real values representing one or more time series signals. The news-signals library supports diverse data
Externí odkaz:
http://arxiv.org/abs/2312.11399
Sentence compression reduces the length of text by removing non-essential content while preserving important facts and grammaticality. Unsupervised objective driven methods for sentence compression can be used to create customized models without the
Externí odkaz:
http://arxiv.org/abs/2205.08221
Sequence-to-sequence (s2s) models are the basis for extensive work in natural language processing. However, some applications, such as multi-document summarization, multi-modal machine translation, and the automatic post-editing of machine translatio
Externí odkaz:
http://arxiv.org/abs/2006.08748
Previous work on automatic news timeline summarization (TLS) leaves an unclear picture about how this task can generally be approached and how well it is currently solved. This is mostly due to the focus on individual subtasks, such as date selection
Externí odkaz:
http://arxiv.org/abs/2005.10107
Multi-document summarization (MDS) aims to compress the content in large document collections into short summaries and has important applications in story clustering for newsfeeds, presentation of search results, and timeline generation. However, the
Externí odkaz:
http://arxiv.org/abs/2005.10070
Autor:
Ghalandari, Demian Gholipour
The centroid-based model for extractive document summarization is a simple and fast baseline that ranks sentences based on their similarity to a centroid vector. In this paper, we apply this ranking to possible summaries instead of sentences and use
Externí odkaz:
http://arxiv.org/abs/1708.07690