Canada

In this paper, we tackle a lay summarization task that aims to produce lay summary
of biomedical articles. BioLaySumm in the BioNLP Workshop at ACL 2023
(Goldsack et al., 2023), has presented us with this lay summarization task for
biomedical articles. Our proposed models provide a three-step abstractive
approach for summarizing biomedical articles. Our methodology involves breaking
down the original document into distinct sections, generating candidate summaries
for each subsection, and then selecting the top-performing paragraph for each
section. We run ablation studies to establish that each step in our pipeline is critical
for improvement in the quality of the lay summary. This model achieved the second-
highest rank in terms of readability scores (Luo et al., 2022). Our work distinguishes
itself from previous studies by not only considering the content of the paper but
also its structure, resulting in more coherent and comprehensible lay summaries.
We hope that our model for generating lay summaries of biomedical articles will be
a useful resource for individuals across various domains, including academia, industry, and healthcare, who require rapid comprehension of key scientific research.

ACL 2023

APTSumm at BioLaySumm Task 1: Biomedical Breakdown, Improving Readability by Relevancy Based Selection

biomedical articles

lay summarization

abstractive summarization

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ACL 2024

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

workshop paper

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
While the impact of Covid has considerably diminished in terms of traveling, obtaining visas to Canada
entails a very long process. Moreover, the global economic conditions pose challenges for many individuals to travel to conferences. Recognizing these circumstances, we know many participants may not be
able to attend the conference in person. Therefore, we are committed to providing a great virtual platform
so everyone has the opportunity to interact with other participants and enjoy the conference. Based on the
current registered participants, approxiately 30% have chosen to attend the conference virtually. Whether
you join us in person or virtually, we sincerely hope everyone has a remarkable conference experience. 
This General Chair’s message is where I express my gratitude to the many individuals who have made
enormous contributions to the conference over the past year.

Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

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The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

HeGeL: A Novel Dataset for Geo-Location from Hebrew Text

In this paper, we present the interim results of a transformer-based annotation pipeline for Ancient and Medieval Greek. As the texts in the Database of Byzantine Book Epigrams have not been normalised, they pose more challenges for manual and automatic annotation than Ancient Greek, normalised texts do. As a result, the existing annotation tools perform poorly. We compiled three data sets for the development of an automatic annotation tool and carried out an inter-annotator agreement study, with a promising agreement score. The experimental results show that our part-of-speech tagger yields accuracy scores that are almost 50 percentage points higher than the widely used rule-based system Morpheus. In addition, error analysis revealed problems related to phenomena also occurring in current social media language.

Medieval Social Media: Manual and Automatic Annotation of Byzantine Greek Marginal Writing

In this paper, we give a brief survey of the difficulties in handling the syntax of mathematical expressions in Universal Dependencies, focusing on examples from English language corpora. We first examine the prevalence and current handling of mathematical expressions in UD corpora. We then examine several strategies for how to approach the handling of syntactic dependencies for such expressions: as multi-word expressions, as a domain appropriate for code-switching, or as approximate to other types of natural language. Ultimately, we argue that mathematical expressions should primarily be analyzed as natural language, and we offer recommendations for the treatment of basic mathematical expressions as analogous to English natural language.

Difficulties in Handling Mathematical Expressions in Universal Dependencies

High-quality labeled data is paramount to the performance of modern machine learning models. However, annotating data is a timeconsuming and costly process that requires human experts to examine large collections of raw data. For conversational agents in production settings with access to large amounts of user-agent conversations, the challenge is to decide what data should be annotated first. We consider the Natural Language Understanding (NLU) component of a conversational agent deployed in a real-world setup with limited resources. We present an active learning pipeline for offline detection of classification errors that leverages two strong classifiers. Then, we perform topic modeling on the potentially mis-classified samples to ease data analysis and to reveal error patterns. In our experiments, we show on a real world dataset that by using our method to prioritize data annotation we reach 100% of the performance annotating only 36% of the data. Finally, we present an analysis of some of the error patterns revealed and argue that our pipeline is a valuable tool to detect critical errors and reduce the workload of annotators.

An Active Learning Pipeline for NLU Error Detection in Conversational Agents

In this project, we have investigated the use of advanced machine learning methods, specifically fine-tuned large language models, for pre-annotating data for a lexical extension task, namely adding descriptive words (verbs) to an existing (but incomplete, as of yet) ontology of event types. Several research questions have been focused on, from the investigation of a possible heuristics to provide at least hints to annotators which verbs to include and which are outside the current version of the ontology, to the possible use of the automatic scores to help the annotators to be more efficient in finding a threshold for identifying verbs that cannot be assigned to any existing class and therefore they are to be used as seeds for a new class. We have also carefully examined the correlation of the automatic scores with the human annotation. While the correlation turned out to be strong, its influence on the annotation proper is modest due to its near linearity, even though the mere fact of such pre-annotation leads to relatively short annotation times.

Extending an Event-type Ontology: Adding Verbs and Classes Using Fine-tuned LLMs Suggestions

Much work in natural language processing (NLP) relies on human annotation. The majority of this implicitly assumes that annotator’s labels are temporally stable, although the reality is that human judgements are rarely con-
sistent over time. 

As a subjective annotation task, hate speech labels depend on annotator’s emotional and moral reactions to the language used to convey the message. Studies in Cognitive Science reveal a ‘foreign language effect’, whereby people take differing moral positions and perceive offensive phrases to be weaker in their second languages. Does this affect annotations as well?

We conduct an experiment to investigate the impacts of (1) time and (2) different language conditions (English and German) on measurements of intra-annotator agreement in a hate speech labelling task. While we do not observe the expected lower stability in the different language condition, we find that overall agreement
is significantly lower than is implicitly assumed in annotation tasks, which has important implications for dataset reproducibility in NLP

Temporal and Second Language Influence on Intra-Annotator Agreement and Stability in Hate Speech Labelling

Annotating cross-document event coreference links is a time-consuming and cognitively demanding task that can compromise annotation quality and efficiency. To address this, we propose a model-in-the-loop annotation approach for event coreference resolution, where a machine learning model suggests likely corefering event pairs only. We evaluate the effectiveness of this approach by first simulating the annotation process and then, using a novel annotator-centric Recall-Annotation effort trade-off metric, we compare the results of various underlying models and datasets. We finally present a method for obtaining 97\% recall while substantially reducing the workload required by a fully manual annotation process.

How Good Is the Model in Model-in-the-loop Event Coreference Resolution Annotation?

This study describes the model design of the NCUEE-NLP system for BioLaySumm Task 2 at the BioNLP 2023 workshop. We separately fine-tune pretrained PRIMERA models to independently generate technical abstracts and lay summaries of biomedical articles. A total of seven evaluation metrics across three criteria were used to compare system performance. Our best submission was ranked first for relevance, second for readability, and fourth for factuality, tying first for overall performance.

NCUEE-NLP at BioLaySumm Task 2: Readability-Controlled Summarization of Biomedical Articles Using the PRIMERA Models

Lay summarization aims to simplify complex scientific information for non-expert audiences. This paper investigates the trade-off between readability and relevance in the lay summarization of long biomedical documents. We introduce a two-stage framework that attains the best readability metrics in the first subtask of BioLaySumm 2023, with 8.924 Flesch–Kincaid Grade Level and 9.188 Dale–Chall Readability Score. However, this comes at the cost of reduced relevance and factuality, emphasizing the inherent challenges of balancing readability and content preservation in lay summarization. The first stage generates summaries using a large language model, such as BART with LSG attention. The second stage uses a zero-shot sentence simplification method to improve the readability of the summaries. In the second subtask, a hybrid dataset is employed to train a model capable of generating both lay summaries and abstracts. This approach achieves the best readability score and shares the top overall rank with other leading methods. Our study underscores the importance of developing effective methods for creating accessible lay summaries while maintaining information integrity. Future work will integrate simplification and summary generation within a joint optimization framework that generates high-quality lay summaries that effectively communicate scientific content to a broader audience.

Pathology Dynamics at BioLaySumm: the trade-off between Readability, Relevance, and Factuality in Lay Summarization

ChatGPT is a large language model developed by OpenAI. Despite its impressive performance across various tasks, no prior work has investigated its capability in the biomedical domain yet. To this end, this paper aims to evaluate the zero-shot performance of ChatGPT on various benchmark biomedical tasks, such as relation extraction, document classification, question answering, and summarization. To the best of our knowledge, this is the first work that conducts an extensive evaluation of ChatGPT in the biomedical domain. Interestingly, we find based on our evaluation that in biomedical datasets that have smaller training sets, zero-shot ChatGPT even outperforms the state-of-the-art fine-tuned generative transformer models, such as BioGPT and BioBART. This suggests that ChatGPT’s pre-training on large text corpora makes it quite specialized even in the biomedical domain. Our findings demonstrate that ChatGPT has the potential to be a valuable tool for various biomedical tasks that lack large annotated data.

Next from ACL 2023

HeGeL: A Novel Dataset for Geo-Location from Hebrew Text

Similar lecture

BioLay_AK_SS at BioLaySumm: Domain Adaptation by Two-Stage Fine-Tuning of Large Language Models used for Biomedical Lay Summary Generation