2,756 research outputs found
Large Language Models and Control Mechanisms Improve Text Readability of Biomedical Abstracts
Biomedical literature often uses complex language and inaccessible
professional terminologies. That is why simplification plays an important role
in improving public health literacy. Applying Natural Language Processing (NLP)
models to automate such tasks allows for quick and direct accessibility for lay
readers. In this work, we investigate the ability of state-of-the-art large
language models (LLMs) on the task of biomedical abstract simplification, using
the publicly available dataset for plain language adaptation of biomedical
abstracts (\textbf{PLABA}). The methods applied include domain fine-tuning and
prompt-based learning (PBL) on: 1) Encoder-decoder models (T5, SciFive, and
BART), 2) Decoder-only GPT models (GPT-3.5 and GPT-4) from OpenAI and BioGPT,
and 3) Control-token mechanisms on BART-based models. We used a range of
automatic evaluation metrics, including BLEU, ROUGE, SARI, and BERTscore, and
also conducted human evaluations. BART-Large with Control Token (BART-L-w-CT)
mechanisms reported the highest SARI score of 46.54 and T5-base reported the
highest BERTscore 72.62. In human evaluation, BART-L-w-CTs achieved a better
simplicity score over T5-Base (2.9 vs. 2.2), while T5-Base achieved a better
meaning preservation score over BART-L-w-CTs (3.1 vs. 2.6). We also categorised
the system outputs with examples, hoping this will shed some light for future
research on this task. Our code, fine-tuned models, and data splits are
available at \url{https://github.com/HECTA-UoM/PLABA-MU}Comment: working pape
Selecting and Generating Computational Meaning Representations for Short Texts
Language conveys meaning, so natural language processing (NLP) requires representations of meaning. This work addresses two broad questions: (1) What meaning representation should we use? and (2) How can we transform text to our chosen meaning representation? In the first part, we explore different meaning representations (MRs) of short texts, ranging from surface forms to deep-learning-based models. We show the advantages and disadvantages of a variety of MRs for summarization, paraphrase detection, and clustering. In the second part, we use SQL as a running example for an in-depth look at how we can parse text into our chosen MR. We examine the text-to-SQL problem from three perspectives—methodology, systems, and applications—and show how each contributes to a fuller understanding of the task.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143967/1/cfdollak_1.pd
CELLS: A Parallel Corpus for Biomedical Lay Language Generation
Recent lay language generation systems have used Transformer models trained
on a parallel corpus to increase health information accessibility. However, the
applicability of these models is constrained by the limited size and topical
breadth of available corpora. We introduce CELLS, the largest (63k pairs) and
broadest-ranging (12 journals) parallel corpus for lay language generation. The
abstract and the corresponding lay language summary are written by domain
experts, assuring the quality of our dataset. Furthermore, qualitative
evaluation of expert-authored plain language summaries has revealed background
explanation as a key strategy to increase accessibility. Such explanation is
challenging for neural models to generate because it goes beyond simplification
by adding content absent from the source. We derive two specialized paired
corpora from CELLS to address key challenges in lay language generation:
generating background explanations and simplifying the original abstract. We
adopt retrieval-augmented models as an intuitive fit for the task of background
explanation generation, and show improvements in summary quality and simplicity
while maintaining factual correctness. Taken together, this work presents the
first comprehensive study of background explanation for lay language
generation, paving the path for disseminating scientific knowledge to a broader
audience. CELLS is publicly available at:
https://github.com/LinguisticAnomalies/pls_retrieval
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