Extractive summarization of clinical trial descriptions

Gülden, Christian; Kirchner, Melanie; Schüttler, Christina; Hinderer, Marc; Kampf, Marvin; Prokosch, Hans‐Ulrich; Toddenroth, Dennis

doi:10.1016/j.ijmedinf.2019.05.019

Cited by 35 publications

(16 citation statements)

References 20 publications

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“…This algorithm has a linear rate and uses the values of the matrix TF-IDF. This method does not need training, and if necessary, it can accumulate information to increase further the accuracy of work using Bayesian estimates of clustering parameters [36], [50].…”

Section: F K-means Clusteringmentioning

confidence: 99%

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Unstructured Text Documents Summarization With Multi-Stage Clustering

et al. 2020

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In natural language processing, text summarization is an important application used to extract desired information by reducing large text. Existing studies use keyword-based algorithms for grouping text, which do not give the documents' actual theme. Our proposed dynamic corpus creation mechanism combines metadata with summarized extracted text. The proposed approach analyzes the mesh of multiple unstructured documents and generates a linked set of multiple weighted nodes by applying multistage Clustering. We have generated adjacency graphs to link the clusters of various collections of documents. This approach comprises of ten steps: pre-processing, making multiple corpuses, first stage clustering, creating sub-corpuses, interlinking sub-corpuses, creating page rank keyword dictionary of each sub-corpus, second stage clustering, path creation among clusters of sub-corpuses, text processing by forward and backward propagation for results generation. The outcome of this technique consists of interlinked subcorpuses through clusters. We have applied our approach to a News dataset, and this interlinked corpus processing follows step by step clustering to search the most relevant parts of the corpus with less cost, time, and improve content detection. We have applied six different metadata processing combinations over multiple text queries to compare results during our experimentation. The comparison results of text satisfaction show that Page-Rank keywords give 38% related text, single-stage Clustering gives 46%, twostage Clustering gives 54%, and the proposed technique gives 67% associated text. Furthermore, this approach covers/searches the relevant data with a range of most to less relevant content. It provides the systematic query-relevant corpus processing mechanism, which automatically selects the most relevant subcorpus through dynamic path selection. We used the SHAP model to evaluate the proposed technique, and our evaluation results proved that the proposed mechanism improved text processing. Moreover, combining text summarization features, shown satisfactory results compared to the summaries generated by general models of abstractive & extractive summarization.

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Section: F K-means Clusteringmentioning

confidence: 99%

“…It recommends those most likely items in which the users are interested. Item similarity recommendation depends on the value of CS [28], [30], [36]. CS has the following formula.…”

Section: G Cosine Similaritymentioning

confidence: 99%

Unstructured Text Documents Summarization With Multi-Stage Clustering

et al. 2020

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“…Several medical domain natural language generation tasks have been studied using machine learning models, including generating radiology reports from images (Jing et al, 2018;Vaswani et al, 2017) and summarizing clinical reports (Zhang et al, 2018;Pivovarov and Elhadad, 2015) or research literature (Cohan et al, 2018). Recently, Gulden et al (2019) studied extractive summarization on RCT descriptions.…”

Section: Medical Natural Language Generationmentioning

confidence: 99%

Towards Understanding of Medical Randomized Controlled Trials by Conclusion Generation

Shieh

Chuang

et al. 2019

Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)

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Randomized controlled trials (RCTs) represent the paramount evidence of clinical medicine. Using machines to interpret the massive amount of RCTs has the potential of aiding clinical decision-making. We propose a RCT conclusion generation task from the PubMed 200k RCT sentence classification dataset to examine the effectiveness of sequence-to-sequence models on understanding RCTs. We first build a pointergenerator baseline model for conclusion generation. Then we fine-tune the state-of-the-art GPT-2 language model, which is pre-trained with general domain data, for this new medical domain task. Both automatic and human evaluation show that our GPT-2 fine-tuned models achieve improved quality and correctness in the generated conclusions compared to the baseline pointer-generator model. Further inspection points out the limitations of this current approach and future directions to explore * .

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“…Recently, automatic text summarization has been used in many fields, such as biomedical domain [4,5], medical domain [6][7][8], and meteorological domain [9]. Automatic procuratorial suggestion document summarization belongs to another application of automatic document summarization, which is to generate short and simplified summaries as procuratorial suggestions from public interest litigation document.…”

Section: Introductionmentioning

confidence: 99%

Automatic Generation of the Draft Procuratorial Suggestions Based on an Extractive Summarization Method: BERTSLCA

Sun

Yang

Wang

et al. 2021

Mathematical Problems in Engineering

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The automatic generation of the draft procuratorial suggestions is to extract the description of illegal facts, administrative omission, description of laws and regulations, and other information from the case documents. Previously, the existing deep learning methods mainly focus on context-free word embeddings when addressing legal domain-speciﬁc extractive summarization tasks, which cannot get a better semantic understanding of the text and in turn leads to an adverse summarization performance. To this end, we propose a novel deep contextualized embeddings-based method BERTSLCA to conduct the extractive summarization task. The model is mainly based on the variant of BERT called BERTSUM. Firstly, the input document is fed into BERTSUM to get sentence-level embeddings. Then, we design an extracting architecture to catch the long dependency between sentences utilizing the Bi-Long Short-Term Memory (Bi-LSTM) unit, and at the end of the architecture, three cascaded convolution kernels with different sizes are designed to extract the relationships between adjacent sentences. Last, we introduce an attention mechanism to strengthen the ability to distinguish the importance of different sentences. To the best of our knowledge, this is the first work to use the pretrained language model for extractive summarization tasks in the field of Chinese judicial litigation. Experimental results on public interest litigation data and CAIL 2020 dataset all demonstrate that the proposed method achieves competitive performance.

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Extractive summarization of clinical trial descriptions

Cited by 35 publications

References 20 publications

Unstructured Text Documents Summarization With Multi-Stage Clustering

Unstructured Text Documents Summarization With Multi-Stage Clustering

Towards Understanding of Medical Randomized Controlled Trials by Conclusion Generation

Automatic Generation of the Draft Procuratorial Suggestions Based on an Extractive Summarization Method: BERTSLCA

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