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The most reliable and up-to-date information on health questions is in the biomedical literature, but inaccessible due to the complex language full of jargon. Domain specific scientific text simplification holds the promise to make this literature accessible to a lay audience. Therefore, we create Cochrane-auto: a large corpus of pairs of aligned sentences, paragraphs, and abstracts from biomedical abstracts and lay summaries. Experiments demonstrate that a plan-guided simplification system trained on Cochrane-auto is able to outperform a strong baseline trained on unaligned abstracts and lay summaries. More generally, our freely available corpus complementing Newsela-auto and Wiki-auto facilitates text simplification research beyond the sentence-level and direct lexical and grammatical revisions.

EMNLP 2024

Cochrane-auto: An Aligned Dataset for the Simplification of Biomedical Abstracts

biomedical text simplification

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Research into automatic text simplification aims to promote access to information for all members of society. To facilitate generalizability, simplification research often abstracts away from specific use cases, and targets a prototypical reader and an underspecified content creator. In this paper, we consider a real-world use case -- simplification technology for use in Dutch municipalities -- and identify the needs of the content creators and the target audiences in this scenario. The stakeholders envision a system that (a) assists the human writer without taking over the task; (b) provides diverse outputs, tailored for specific target audiences; and (c) explains the suggestions that it outputs. These requirements call for technology that is characterized by modularity, explainability, and variability. We argue that these are important research directions that require further exploration.

Considering Human Interaction and Variability in Automatic Text Simplification

Biomedical literature is often written in highly specialized language, posing significant comprehension challenges for non-experts. Automatic text simplification (ATS) offers a solution by making such texts more accessible while preserving critical information. However, evaluating ATS for biomedical texts is still challenging due to the limitations of existing evaluation metrics. General-domain metrics like SARI, BLEU, and ROUGE focus on surface-level text features, and readability metrics like FKGL and ARI fail to account for domain-specific terminology or assess how well the simplified text conveys core meanings (gist). To address this, we introduce SciGisPy, a novel evaluation metric inspired by Gist Inference Score (GIS) from Fuzzy-Trace Theory (FTT). SciGisPy measures how well a simplified text facilitates the formation of abstract inferences (gist) necessary for comprehension, especially in the biomedical domain. We revise GIS for this purpose by introducing domain-specific enhancements, including semantic chunking, Information Content (IC) theory, and specialized embeddings, while removing unsuitable indexes. Our experimental evaluation on the Cochrane biomedical text simplification dataset demonstrates that SciGisPy outperforms the original GIS formulation, with a significant increase in correctly identified simplified texts (84% versus 44.8%). The results and a thorough ablation study confirm that SciGisPy better captures the essential meaning of biomedical content, outperforming existing approaches.

SciGisPy: a Novel Metric for Biomedical Text Simplification via Gist Inference Score

This research focuses on evaluating the simplification of Brazilian legal rulings using large language models (LLMs). The main question addressed by the study is whether large language models can automatically simplify Brazilian legal texts. Most simplification methods are validated by comparing them to human-made simplifications. However, such resources are limited for Brazilian Portuguese, particularly in the legal domain, making this task both challenging and significant for advancing language technologies in this field. The findings show that the models are capable of simplifying legal sentences, but there is a trade-off. While both models reduce the complexity of the language used, they also decrease the semantic similarity with the original text, highlighting the challenge of simplifying text while maintaining its core meaning.

Evaluating the Simplification of Brazilian Legal Rulings in LLMs Using Readability Scores as a Target

Medical text simplification is crucial for making complex biomedical literature more accessible to non-experts. Traditional methods struggle with the specialized terms and jargon of medical texts, lacking the flexibility to adapt the simplification process dynamically. In contrast, recent advancements in large language models (LLMs) present unique opportunities by offering enhanced control over text simplification through iterative refinement and collaboration between specialized agents. In this work, we introduce the Society of Medical Simplifiers, a novel LLM-based framework inspired by the "Society of Mind" (SOM) philosophy. Our approach leverages the strengths of LLMs by assigning five distinct roles, i.e., Layperson, Simplifier, Medical Expert, Language Clarifier, and Redundancy Checker, organized into interaction loops. This structure allows the agents to progressively improve text simplification while maintaining the complexity and accuracy of the original content. Evaluations on the Cochrane text simplification dataset demonstrate that our framework is on par with or outperforms state-of-the-art methods, achieving superior readability and content preservation through controlled simplification processes.

Society of Medical Simplifiers

The tasks of lexical complexity prediction (LCP) and complex word identification (CWI) commonly presuppose that difficult to understand words are shared by the target population. Meanwhile, personalization methods have also been proposed to adapt models to individual needs. We verify that a recent Japanese LCP dataset is representative of its target population by partially replicating the annotation. By another reannotation we show that native Chinese speakers perceive the complexity differently due to Sino-Japanese vocabulary. To explore the possibilities of personalization, we compare competitive baselines trained on the group mean ratings and individual ratings in terms of performance for an individual. We show that the model trained on a group mean performs similarly to an individual model in the CWI task, while achieving good LCP performance for an individual is difficult. We also experiment with adapting a finetuned BERT model, which results only in marginal improvements across all settings.

Difficult for Whom? A Study of Japanese Lexical Complexity

Vision-language models (VLMs) have recently shown promising results in traditional downstream tasks. The capability of VLMs to ``think'' from a first-person perspective, a crucial attribute for advancing autonomous agents and robotics, remains largely unexplored. To bridge this research gap, we introduce EgoThink, a novel visual question-answering benchmark that encompasses six core capabilities with twelve detailed dimensions. The benchmark is constructed using selected clips from egocentric videos, with manually annotated question-answer pairs containing first-person information. To comprehensively assess VLMs, we evaluate twenty-one popular VLMs on EgoThink. Moreover, given the open-ended format of the answers, we use GPT-4 as the automatic judge to compute single-answer grading. Experimental results indicate that although GPT-4V leads in numerous dimensions, all evaluated VLMs still possess considerable potential for improvement in first-person perspective tasks.

EgoThink: Evaluating First-Person Perspective Thinking Capability of Vision-Language Models

Large Language Models (LLMs) for Bangla text summarization condense texts while pre serving key information by leveraging ad vanced Natural Language Processing (NLP) techniques. In this study, we used two popular Bangla news summarization datasets through the evaluation of 5 LLMs as well as human evaluation. we made two key observations. First, we found that GPT-4 with zero-shot model settings performs well in Bangla news summarization. Secondly, previous research has been constrained by low-quality references, resulting in an underestimation of human per formance and diminished few-shot capabilities. To more accurately evaluate LLMs, we per formed human assessments using high-quality summaries created by student writers. De spite notable stylistic differences, including the extent of paraphrasing, LLM-generated sum maries were found to be comparable to those written by humans. Our model was assessed both qualitatively and quantitatively, and com parisons with other published results showed significant improvements in human evaluation scores due to the LLM techniques.

Evaluating Large Language Models for Summarizing Bangla Texts

Torwali [ISO 639-3: trw] is an endangered and indigenous language spoken in North of Pakistan. It is a low-resource language writ-ten in RTL Perso-Arabic script. This paper discusses the challenges and approaches in processing of Torwali with various NLP tech-niques to develop tools and resources. This work contributes towards morphological analysis, word segmentation, POS tagging and transliteration of Torwali. This work, on which this paper is based, can be used as a resource for other lexically similar endan-gered languages of northern Pakistan and will help to improve the digital presence of Tor-wali language and will safeguard it against endangerment

Developing Language Technology and NLP tools for endangered languages: Torwali

This study explores Pre-trained Language Models (PLMs) for Arabic mental health question answering using the novel MentalQA dataset. We establish a baseline for future research and compare PLMs to classical models. Fine-tuned PLMs outperform classical models, with MARBERT achieving the best results (0.89 F1-score). Few-shot learning with GPT models also shows promise. This work highlights PLMs' potential for Arabic mental health applications while identifying areas for further development.

A Comparative Study of Using Pre-trained Language Models for Mental Healthcare Q&A Classification in Arabic

This study focuses on developing a sentence-level automatic speech segmentation system for Amharic. Two approaches were explored. The first approach utilized an automatic tool for segmenting and labeling Amharic speech data, creating an acoustic model through HMM modeling. The system's segmentation was refined using forced alignment AdaBoost techniques. In the second approach, prosodic features were extracted directly from the speech waveform, and statistical methods including AdaBoost were employed. Additionally, LSTM and Bi-LSTM models were utilized, achieving impressive accuracies of 94.62% and 95.23%, respectively. These approaches contribute to advancing automatic speech segmentation for Amharic, promising improved accuracy and efficiency.

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Cochrane-auto: An Aligned Dataset for the Simplification of Biomedical Abstracts

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