Attend to Medical Ontologies: Content Selection for Clinical Abstractive Summarization (2020)

Abstract: Sequence-to-sequence (seq2seq) network is a well-established model for text summarization task. It can learn to produce readable content; however, it falls short in effectively identifying key regions of the source. In this paper, we approach the content selection problem for clinical abstractive summarization by augmenting salient ontological terms into the summarizer. Our experiments on two publicly available clinical data sets (107,372 reports of MIMIC-CXR, and 3,366 reports of OpenI) show that our model statistically significantly boosts state-of-the-art results in terms of ROUGE metrics (with improvements: 2.9% RG-1, 2.5% RG-2, 1.9% RG-L), in the healthcare domain where any range of improvement impacts patients’ welfare.

## introduction radiology reports convey the detailed observations along with the significant findings about a medical encounter. each radiology report contains two important sections: 1 findings that encompasses radiologist's detailed observations and interpretation of imaging study, and impression summarizing the most critical findings. impression (usually couple of lines and thrice smaller than finding) is considered as the most integral part of report @xcite as it plays a key role in communicating critical findings to referring clinicians. previous studies have reported that clinicians mostly read the impression as they have less time to review findings, particularly those that are lengthy or intricate @xcite . in clinical setting, generating impression from findings can be subject to errors @xcite . this fact is especially crucial when it comes to healthcare domain where even 1 depending on institution, radiology reports may or may not include other fields such as background. the smallest improvement in generating impres-sion can improve patients' well-being. automating the process of impression generation in radiology reporting would save clinicians' read time and decrease fatigue @xcite as clinicians would only need to proofread summaries or make minor edits. previously, @xcite showed that augmenting the summarizer with entire ontology (i.e., clinical) terms within the findings can improve the content selection and summary generation to some noticeable extent. our findings, further, suggest that radiologists select significant ontology terms, but not all such terms, to write the impression. following this paradigm, we hypothesize that selecting the most significant clinical terms occurring in the findings and then incorporating them into the summarization would improve the final impression generation. we further examine if refining findings word representations according to the identified clinical terms would result in improved impression generation. overall, the contributions of this work are twofold: (i) we propose a novel seq2seq-based model to incorporate the salient clinical terms into the summarizer ( §3.2). we pose copying likelihood of a word as an indicator of its saliency in terms of forming impression, which can be learned via a sequence-tagger ( §3.1); (ii) our model statistically significantly improves over the competitive baselines on mimic-cxr publicly available clinical dataset. to evaluate the cross-organizational transferability, we further evaluate our model on another publicly available clinical dataset (openi) ( §5). ## related work few prior studies have pointed out that although seq2seq models can effectively produce readable content, they perform poorly at selecting salient content to include in the summary @xcite . many attempts have been made to tackle this problem @xcite @xcite @xcite . for example, @xcite used sentence representations to filter secondary information of word representation. our work is different in that we utilize ontology representations produced by an additional encoder to filter word representations. @xcite utilized a data-efficient content selector, by aligning source and target, to restrict the model's attention to likely-to-copy phrases. in contrast, we use the content selector to find domain knowledge alignment between source and target. moreover, we do not focus on model attention here, but on rectifying word representations. extracting clinical findings from clinical reports has been explored previously @xcite . for summarizing radiology reports, @xcite al. (2018 ) recently used a separate rnn to encode a section of radiology report. 2 subsequently, macavaney et al. (2019) extracted clinical ontologies within the findings to help the model learn these useful signals by guiding decoder in generation process. our work differs in that we hypothesize that all of the ontological terms in the findings are not equally important, but there is a notion of odds of saliency for each of these terms; thus, we focus on refining the findings representations. ## model our model consists of two main components: (1) a content selector to identify the most salient ontological concepts specific to a given report, and (2) a summarization model that incorporates the identified ontology terms within the findings into the summarizer. the summarizer refines the findings word representation based on salient ontology word representation encoded by a separate encoder. ## conclusion we proposed an approach to content selection for abstractive text summarization in clinical notes. we introduced our novel approach to augment standard summarization model with significant ontological terms within the source. content selection problem is framed as a word-level sequence-tagging task. the intrinsic evaluations on two publicly available real-life clinical datasets show the efficacy of our model in terms of rouge metrics. furthermore, the extrinsic evaluation by domain experts further reveals the qualities of our system-generated summaries in comparison with gold summaries.