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pro vyhledávání: '"Shardlow A"'
Autor:
Vásquez-Rodríguez, Laura, Nguyen, Nhung T. H., Przybyła, Piotr, Shardlow, Matthew, Ananiadou, Sophia
In this paper, we present the SimDoc system, a simplification model considering simplicity, readability, and discourse aspects, such as coherence. In the past decade, the progress of the Text Simplification (TS) field has been mostly shown at a sente
Externí odkaz:
http://arxiv.org/abs/2412.18655
Autor:
Ventirozos, Filippos, Nteka, Ioanna, Nandy, Tania, Baca, Jozef, Appleby, Peter, Shardlow, Matthew
This paper presents a case study on the development of Auto-AdvER, a specialised named entity recognition schema and dataset for text in the car advertisement genre. Developed with industry needs in mind, Auto-AdvER is designed to enhance text mining
Externí odkaz:
http://arxiv.org/abs/2412.05655
Priors with non-smooth log densities have been widely used in Bayesian inverse problems, particularly in imaging, due to their sparsity inducing properties. To date, the majority of algorithms for handling such densities are based on proximal Langevi
Externí odkaz:
http://arxiv.org/abs/2411.11403
Overview of the BioLaySumm 2024 Shared Task on the Lay Summarization of Biomedical Research Articles
This paper presents the setup and results of the second edition of the BioLaySumm shared task on the Lay Summarisation of Biomedical Research Articles, hosted at the BioNLP Workshop at ACL 2024. In this task edition, we aim to build on the first edit
Externí odkaz:
http://arxiv.org/abs/2408.08566
Autor:
Li, Zihao, Belkadi, Samuel, Micheletti, Nicolo, Han, Lifeng, Shardlow, Matthew, Nenadic, Goran
In this system report, we describe the models and methods we used for our participation in the PLABA2023 task on biomedical abstract simplification, part of the TAC 2023 tracks. The system outputs we submitted come from the following three categories
Externí odkaz:
http://arxiv.org/abs/2408.03871
We derive an algorithm for compression of the currents and varifolds representations of shapes, using the Nystrom approximation in Reproducing Kernel Hilbert Spaces. Our method is faster than existing compression techniques, and comes with theoretica
Externí odkaz:
http://arxiv.org/abs/2406.09932
Lexical Simplification (LS) automatically replaces difficult to read words for easier alternatives while preserving a sentence's original meaning. LS is a precursor to Text Simplification with the aim of improving text accessibility to various target
Externí odkaz:
http://arxiv.org/abs/2402.14972
Autor:
Kew, Tannon, Chi, Alison, Vásquez-Rodríguez, Laura, Agrawal, Sweta, Aumiller, Dennis, Alva-Manchego, Fernando, Shardlow, Matthew
We present BLESS, a comprehensive performance benchmark of the most recent state-of-the-art large language models (LLMs) on the task of text simplification (TS). We examine how well off-the-shelf LLMs can solve this challenging task, assessing a tota
Externí odkaz:
http://arxiv.org/abs/2310.15773
The use of optimal transport (OT) distances, and in particular entropic-regularised OT distances, is an increasingly popular evaluation metric in many areas of machine learning and data science. Their use has largely been driven by the availability o
Externí odkaz:
http://arxiv.org/abs/2310.05019
Autor:
Goldsack, Tomas, Luo, Zheheng, Xie, Qianqian, Scarton, Carolina, Shardlow, Matthew, Ananiadou, Sophia, Lin, Chenghua
Publikováno v:
The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks (2023) 468-477
This paper presents the results of the shared task on Lay Summarisation of Biomedical Research Articles (BioLaySumm), hosted at the BioNLP Workshop at ACL 2023. The goal of this shared task is to develop abstractive summarisation models capable of ge
Externí odkaz:
http://arxiv.org/abs/2309.17332