Asma Ben Abacha


2020

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Visual Question Generation from Radiology Images
Mourad Sarrouti | Asma Ben Abacha | Dina Demner-Fushman
Proceedings of the First Workshop on Advances in Language and Vision Research

Visual Question Generation (VQG), the task of generating a question based on image contents, is an increasingly important area that combines natural language processing and computer vision. Although there are some recent works that have attempted to generate questions from images in the open domain, the task of VQG in the medical domain has not been explored so far. In this paper, we introduce an approach to generation of visual questions about radiology images called VQGR, i.e. an algorithm that is able to ask a question when shown an image. VQGR first generates new training data from the existing examples, based on contextual word embeddings and image augmentation techniques. It then uses the variational auto-encoders model to encode images into a latent space and decode natural language questions. Experimental automatic evaluations performed on the VQA-RAD dataset of clinical visual questions show that VQGR achieves good performances compared with the baseline system. The source code is available at https://github.com/sarrouti/vqgr.

2019

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On the Summarization of Consumer Health Questions
Asma Ben Abacha | Dina Demner-Fushman
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Question understanding is one of the main challenges in question answering. In real world applications, users often submit natural language questions that are longer than needed and include peripheral information that increases the complexity of the question, leading to substantially more false positives in answer retrieval. In this paper, we study neural abstractive models for medical question summarization. We introduce the MeQSum corpus of 1,000 summarized consumer health questions. We explore data augmentation methods and evaluate state-of-the-art neural abstractive models on this new task. In particular, we show that semantic augmentation from question datasets improves the overall performance, and that pointer-generator networks outperform sequence-to-sequence attentional models on this task, with a ROUGE-1 score of 44.16%. We also present a detailed error analysis and discuss directions for improvement that are specific to question summarization.

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Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering
Asma Ben Abacha | Chaitanya Shivade | Dina Demner-Fushman
Proceedings of the 18th BioNLP Workshop and Shared Task

This paper presents the MEDIQA 2019 shared task organized at the ACL-BioNLP workshop. The shared task is motivated by a need to develop relevant methods, techniques and gold standards for inference and entailment in the medical domain, and their application to improve domain specific information retrieval and question answering systems. MEDIQA 2019 includes three tasks: Natural Language Inference (NLI), Recognizing Question Entailment (RQE), and Question Answering (QA) in the medical domain. 72 teams participated in the challenge, achieving an accuracy of 98% in the NLI task, 74.9% in the RQE task, and 78.3% in the QA task. In this paper, we describe the tasks, the datasets, and the participants’ approaches and results. We hope that this shared task will attract further research efforts in textual inference, question entailment, and question answering in the medical domain.

2017

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NLM_NIH at SemEval-2017 Task 3: from Question Entailment to Question Similarity for Community Question Answering
Asma Ben Abacha | Dina Demner-Fushman
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes our participation in SemEval-2017 Task 3 on Community Question Answering (cQA). The Question Similarity subtask (B) aims to rank a set of related questions retrieved by a search engine according to their similarity to the original question. We adapted our feature-based system for Recognizing Question Entailment (RQE) to the question similarity task. Tested on cQA-B-2016 test data, our RQE system outperformed the best system of the 2016 challenge in all measures with 77.47 MAP and 80.57 Accuracy. On cQA-B-2017 test data, performances of all systems dropped by around 30 points. Our primary system obtained 44.62 MAP, 67.27 Accuracy and 47.25 F1 score. The cQA-B-2017 best system achieved 47.22 MAP and 42.37 F1 score. Our system is ranked sixth in terms of MAP and third in terms of F1 out of 13 participating teams.

2016

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A Hybrid Approach to Generation of Missing Abstracts in Biomedical Literature
Suchet Chachra | Asma Ben Abacha | Sonya Shooshan | Laritza Rodriguez | Dina Demner-Fushman
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Readers usually rely on abstracts to identify relevant medical information from scientific articles. Abstracts are also essential to advanced information retrieval methods. More than 50 thousand scientific publications in PubMed lack author-generated abstracts, and the relevancy judgements for these papers have to be based on their titles alone. In this paper, we propose a hybrid summarization technique that aims to select the most pertinent sentences from articles to generate an extractive summary in lieu of a missing abstract. We combine i) health outcome detection, ii) keyphrase extraction, and iii) textual entailment recognition between sentences. We evaluate our hybrid approach and analyze the improvements of multi-factor summarization over techniques that rely on a single method, using a collection of 295 manually generated reference summaries. The obtained results show that the hybrid approach outperforms the baseline techniques with an improvement of 13% in recall and 4% in F1 score.

2015

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LIST-LUX: Disorder Identification from Clinical Texts
Asma Ben Abacha | Aikaterini Karanasiou | Yassine Mrabet | Julio Cesar Dos Reis
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

2012

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Extraction d’information automatique en domaine médical par projection inter-langue : vers un passage à l’échelle (Automatic Information Extraction in the Medical Domain by Cross-Lingual Projection) [in French]
Asma Ben Abacha | Pierre Zweigenbaum | Aurélien Max
Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 2: TALN

2011

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Medical Entity Recognition: A Comparaison of Semantic and Statistical Methods
Asma Ben Abacha | Pierre Zweigenbaum
Proceedings of BioNLP 2011 Workshop