Natural Language Processing Knowledge Network Approach for Interactive Highlighting and Summary

Dudko, Alexander; Endrjukaite, Tatiana; Kiyoki, Yasushi

doi:10.1007/978-3-030-12450-2_18

Cited by 1 publication

(3 citation statements)

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“…Automatic text highlighting of medically relevant information has potential to significantly improve the effectiveness and efficiency of healthcare practitioners since physicians would spend less time examining full text and only focus on key words and segments [8]. Many other domains have benefited from automated text highlighting.…”

Section: Introductionmentioning

confidence: 99%

“…Researchers in information management and psychology have shown that text highlighting can improve the reading time [34]. Previous studies have also examined the benefits of text highlighting in other areas such as supporting workers in the digitization of tasks by highlighting key fields [3], requesting highlights as evidence and reasoning to support judgements [29], recommending text passages to facilitate the job of text annotators [33], explaining the output to different machine learning models [21], and highlighting key terms in medical documents [8].…”

Section: Introductionmentioning

confidence: 99%

“…Using domain concepts to form summaries is another method for sentence extraction. For instance, Dudko et al [8] present a new approach of data retrieval from documents in the medical domain. This approach generates a summary of the document and highlights the most important words using document ontology which includes information about all recognized keywords and concepts in the document.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Word-level Text Highlighting of Medical Texts for Telehealth Services

Ozyegen¹,

Kabe²,

Çevik³

2021

Preprint

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The medical domain is often subject to information overload. The digitization of healthcare, constant updates to online medical repositories, and increasing availability of biomedical datasets make it challenging to effectively analyze the data. This creates additional work for medical professionals who are heavily dependent on medical data to complete their research and consult their patients. This paper aims to show how different text highlighting techniques can capture relevant medical context. This would reduce the doctors' cognitive load and response time to patients by facilitating them in making faster decisions, thus improving the overall quality of online medical services.Three different word-level text highlighting methodologies are implemented and evaluated. The first method uses TF-IDF scores directly to highlight important parts of the text. The second method is a combination of TF-IDF scores and the application of Local Interpretable Model-Agnostic Explanations to classification models. The third method uses neural networks directly to make predictions on whether or not a word should be highlighted. The results of our experiments show that the neural network approach is success-

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Section: Introductionmentioning

confidence: 99%