SumPubMed: Summarization Dataset of PubMed Scientific Articles

Gupta, Vivek; Bharti, Prerna; Nokhiz, Pegah; Karnick, Harish

doi:10.18653/v1/2021.acl-srw.30

Cited by 22 publications

(13 citation statements)

References 17 publications

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“…NLP is also positioned to address challenges in interpreting documentation in the EHR by facilitating improved communication and understanding of clinician notes. NLP techniques centered around word embeddings have recently been utilized to develop question-answering ( 111 – 114 ), as well as summarizing large bodies of text such as clinical notes ( 52 – 54 ), and scientific publications ( 55 , 115 ). With the advent of the Open Notes movement, a movement supporting transparent documentation among patients, families, and clinicians ( 116 – 118 ), and the 21st Century Cures Act of 2021 ( 119 ), which mandated patient accessibility to their clinical notes, there has been an increasing emphasis on patient involvement and advocacy in their own care.…”

Section: Discussionmentioning

confidence: 99%

“…been utilized to develop question-answering (111-114), as well as summarizing large bodies of text such as clinical notes (52-54), and scientific publications (55,115). With the advent of the Open Notes movement, a movement supporting transparent documentation among patients, families, and clinicians (116)(117)(118), and the 21st Century Cures Act of 2021 (119), which mandated patient accessibility to their clinical notes, there has been an increasing emphasis on patient involvement and advocacy in their own care.…”

Section: Discussionmentioning

confidence: 99%

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Applications of natural language processing in ophthalmology: present and future

Chen

Baxter²

2022

Front. Med.

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Advances in technology, including novel ophthalmic imaging devices and adoption of the electronic health record (EHR), have resulted in significantly increased data available for both clinical use and research in ophthalmology. While artificial intelligence (AI) algorithms have the potential to utilize these data to transform clinical care, current applications of AI in ophthalmology have focused mostly on image-based deep learning. Unstructured free-text in the EHR represents a tremendous amount of underutilized data in big data analyses and predictive AI. Natural language processing (NLP) is a type of AI involved in processing human language that can be used to develop automated algorithms using these vast quantities of available text data. The purpose of this review was to introduce ophthalmologists to NLP by (1) reviewing current applications of NLP in ophthalmology and (2) exploring potential applications of NLP. We reviewed current literature published in Pubmed and Google Scholar for articles related to NLP and ophthalmology, and used ancestor search to expand our references. Overall, we found 19 published studies of NLP in ophthalmology. The majority of these publications (16) focused on extracting specific text such as visual acuity from free-text notes for the purposes of quantitative analysis. Other applications included: domain embedding, predictive modeling, and topic modeling. Future ophthalmic applications of NLP may also focus on developing search engines for data within free-text notes, cleaning notes, automated question-answering, and translating ophthalmology notes for other specialties or for patients, especially with a growing interest in open notes. As medicine becomes more data-oriented, NLP offers increasing opportunities to augment our ability to harness free-text data and drive innovations in healthcare delivery and treatment of ophthalmic conditions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Applications of natural language processing in ophthalmology: present and future

Chen

Baxter²

2022

Front. Med.

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“…Among them is the open-source data set of biomedical research papers collected by PubMed ( 46 ). This data set is widely used for a great variety of NLP tasks including text summarization ( 47 ) as well as general-purpose transformer pre-training ( 48 ). Furthermore, the vast amounts of social media data have proven resourceful in analyzing real-world data of post-acute COVID-19 ( 49 ).…”

Section: Ai For Text and Sequence-based Infection Biology Datamentioning

confidence: 99%

Toward the novel AI tasks in infection biology

Yakimovich

2024

mSphere

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Machine learning and artificial intelligence (AI) are becoming more common in infection biology laboratories around the world. Yet, as they gain traction in research, novel frontiers arise. Novel artificial intelligence algorithms are capable of addressing advanced tasks like image generation and question answering. However, similar algorithms can prove useful in addressing advanced questions in infection biology like prediction of host-pathogen interactions or inferring virus protein conformations. Addressing such tasks requires large annotated data sets, which are often scarce in biomedical research. In this review, I bring together several successful examples where such tasks were addressed. I underline the importance of formulating novel AI tasks in infection biology accompanied by freely available benchmark data sets to address these tasks. Furthermore, I discuss the current state of the field and potential future trends. I argue that one such trend involves AI tools becoming more versatile.

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“…QuestEval examines to what extent the questions from a candidate summary can be answered by its source, taking into account the named entities and nouns from the source document as the ground-truth answers 5 .…”

Section: B Evaluation Metricsmentioning

confidence: 99%

“…the indications for surgery were an aortic stenosis with valve area less than . 5 Table IV shows that these abstractive summarization model. can recapitulate the critical phrases as bullet points.…”

Section: E Case Studymentioning

confidence: 99%

Leveraging Summary Guidance on Medical Report Summarization

Zhu¹,

Yang²,

Wu³

et al. 2023

Preprint

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This study presents three deidentified large medical text datasets, named DISCHARGE, ECHO and RADIOLOGY, which contain 50K, 16K and 378K pairs of report and summary that are derived from MIMIC-III, respectively. We implement convincing baselines of automated abstractive summarization on the proposed datasets with pre-trained encoder-decoder language models, including BERT2BERT, T5-large and BART. Further, based on the BART model, we leverage the sampled summaries from the train set as prior knowledge guidance, for encoding additional contextual representations of the guidance with the encoder and enhancing the decoding representations in the decoder. The experimental results confirm the improvement of ROUGE scores and BERTScore made by the proposed method, outperforming the larger model T5-large.

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SumPubMed: Summarization Dataset of PubMed Scientific Articles

Cited by 22 publications

References 17 publications

Applications of natural language processing in ophthalmology: present and future

Applications of natural language processing in ophthalmology: present and future

Toward the novel AI tasks in infection biology

Leveraging Summary Guidance on Medical Report Summarization

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