{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T14:16:38Z","timestamp":1780323398020,"version":"3.54.1"},"reference-count":70,"publisher":"ASME International","issue":"6","license":[{"start":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T00:00:00Z","timestamp":1652140800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.asme.org\/publications-submissions\/publishing-information\/legal-policies"}],"content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In this study, the extractive summarization using sentence embeddings generated by the finetuned Bidirectional Encoder Representations from Transformers (BERT) models and the k-means clustering method has been investigated. To show how the BERT model can capture the knowledge in specific domains like engineering design and what it can produce after being finetuned based on domain-specific data sets, several BERT models are trained, and the sentence embeddings extracted from the finetuned models are used to generate summaries of a set of papers. Different evaluation methods are then applied to measure the quality of summarization results. Both the machine evaluation method Recall-Oriented Understudy for Gisting Evaluation (ROUGE) and a human-based evaluation method are used for the comparison study. The results indicate that the BERT model finetuned with a larger dataset can generate summaries with more domain terminologies than the pretrained BERT model. Moreover, the summaries generated by BERT models have more contents overlapping with original documents than those obtained through other popular non-BERT-based models. The experimental results indicate that the BERT-based method can provide better and more informative summaries to engineers. It has also been demonstrated that the contextualized representations generated by BERT-based models can capture information in text and have better performance in applications like text summarizations after being trained by domain-specific data sets.<\/jats:p>","DOI":"10.1115\/1.4054203","type":"journal-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T07:38:07Z","timestamp":1648539487000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":11,"title":["Engineering Document Summarization: A Bidirectional Language Model-Based Approach"],"prefix":"10.1115","volume":"22","author":[{"given":"Yunjian","family":"Qiu","sequence":"first","affiliation":[{"name":"Department of Aerospace and Mechanical Engineering, University of Southern California, 3650 McClintock Avenue, OHE 400, Los Angeles, CA, 90089-1453"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Aerospace and Mechanical Engineering, University of Southern California, 3650 McClintock Avenue, OHE 400, Los Angeles, CA, 90089-1453"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"33","published-online":{"date-parts":[[2022,5,10]]},"reference":[{"issue":"3","key":"2022051016584927300_CIT0001","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.ymeth.2015.01.015","article-title":"Application of Text Mining in the Biomedical Domain","volume":"74","author":"Fleuren","year":"2015","journal-title":"Methods"},{"issue":"14","key":"2022051016584927300_CIT0002","doi-asserted-by":"publisher","first-page":"5755","DOI":"10.1016\/j.eswa.2013.04.023","article-title":"2013. Assessing Sentence Scoring Techniques for Extractive Text Summarization","volume":"40","author":"Ferreira","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"2022051016584927300_CIT0003","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-011-9216-z","article-title":"Text Summarisation in Progress: A Literature Review","volume":"37","author":"Lloret","year":"2012","journal-title":"Artif. Intell. Rev."},{"issue":"12","key":"2022051016584927300_CIT0004","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1016\/j.jbi.2014.06.009","article-title":"Text Summarization in the Biomedical Domain: A Systematic Review of Recent Research","volume":"52","author":"Mishra","year":"2014","journal-title":"J. Biomed. Inform."},{"issue":"6","key":"2022051016584927300_CIT0005","doi-asserted-by":"publisher","first-page":"1765","DOI":"10.1016\/j.ipm.2007.01.026","article-title":"The Use of Domain-Specific Concepts in Biomedical Text Summarization","volume":"43","author":"Reeve","year":"2007","journal-title":"Inf. Process. Manag."},{"issue":"1","key":"2022051016584927300_CIT0006","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artmed.2011.06.005","article-title":"A Semantic Graph-Based Approach to Biomedical Summarization","volume":"53","author":"Plaza","year":"2011","journal-title":"Artif. Intell. Med."},{"issue":"5","key":"2022051016584927300_CIT0007","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.jbi.2017.03.007","article-title":"Using Ontology-Based Semantic Similarity to Facilitate the Article Screening Process for Systematic Reviews","volume":"69","author":"Ji","year":"2017","journal-title":"J. Biomed. Inform."},{"key":"2022051016584927300_CIT0008","first-page":"31","article-title":"Extractive Summarization Using Continuous Vector Space Models","author":"K\u00e5geb\u00e4ck","year":"2014"},{"issue":"1","key":"2022051016584927300_CIT0009","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1613\/jair.1.11259","article-title":"From Word to Sense Embeddings: A Survey on Vector Representations of Meaning","volume":"63","author":"Camacho-Collados","year":"2018","journal-title":"J. Artif. Intell. Res."},{"key":"2022051016584927300_CIT0010","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/P16-1046","article-title":"Neural summarization by extracting sentences and words","author":"Cheng","year":"2016"},{"issue":"6","key":"2022051016584927300_CIT0011","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.eswa.2019.01.037","article-title":"Enhancing Unsupervised Neural Networks-Based Text Summarization With Word Embedding and Ensemble Learning","volume":"123","author":"Alami","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"2022051016584927300_CIT0012","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2018"},{"key":"2022051016584927300_CIT0013","first-page":"789","article-title":"Pretraining-Based Natural Language Generation for Text Summarization","author":"Zhang","year":"2019"},{"key":"2022051016584927300_CIT0014","article-title":"Leveraging BERT for Extractive Text Summarization on Lectures","volume-title":"CoRR","author":"Miller","year":"2019"},{"key":"2022051016584927300_CIT0015","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-031-02165-7","volume-title":"Neural Network Methods for Natural Language Processing","author":"Goldberg","year":"2017"},{"key":"2022051016584927300_CIT0016","article-title":"Efficient estimation of word representations in vector space","author":"Mikolov","year":"2013"},{"key":"2022051016584927300_CIT0017","first-page":"1299","article-title":"Two\/too Simple Adaptations of Word2vec for Syntax Problems","author":"Ling","year":"2015"},{"key":"2022051016584927300_CIT0018","article-title":"Bridging Nonlinearities and Stochastic Regularizers With Gaussian Error Linear Units","volume-title":"CoRR","author":"Hendrycks","year":"2016"},{"key":"2022051016584927300_CIT0019","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/N18-1202","article-title":"Deep Contextualized Word Representations","author":"Peters","year":"2018"},{"key":"2022051016584927300_CIT0020","author":"Radford","year":"2018"},{"key":"2022051016584927300_CIT0021","article-title":"Universal language model finetuning for text classification","author":"Howard"},{"key":"2022051016584927300_CIT0022","article-title":"Attention Is All You Need","author":"Vaswani","year":"2017"},{"issue":"8","key":"2022051016584927300_CIT0023","first-page":"9","article-title":"Language Models are Unsupervised Multitask Learners","volume":"1","author":"Radford","year":"2019","journal-title":"OpenAI blog"},{"key":"2022051016584927300_CIT0024","article-title":"Language Models are Few-Shot Learners","author":"Brown","year":"2020"},{"key":"2022051016584927300_CIT0025","author":"Zhu","year":"2021"},{"key":"2022051016584927300_CIT0026","first-page":"19","article-title":"Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books","author":"Zhu","year":"2015"},{"issue":"2","key":"2022051016584927300_CIT0027","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1007\/s10115-017-1042-4","article-title":"Recent Advances in Document Summarization","volume":"53","author":"Yao","year":"2017","journal-title":"Knowl. Inf. Syst."},{"key":"2022051016584927300_CIT0028","first-page":"1638","article-title":"Contextual String Embeddings for Sequence Labeling","author":"Akbik","year":"2018"},{"issue":"4","key":"2022051016584927300_CIT0029","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","article-title":"BioBERT: A Pre-trained Biomedical Language Representation Model for Biomedical Text Mining","volume":"36","author":"Lee","year":"2020","journal-title":"Bioinformatics"},{"key":"2022051016584927300_CIT0030","article-title":"Publicly available clinical BERT embeddings","author":"Alsentzer","year":"2019"},{"issue":"11","key":"2022051016584927300_CIT0031","doi-asserted-by":"publisher","first-page":"1297","DOI":"10.1093\/jamia\/ocz096","article-title":"Enhancing Clinical Concept Extraction With Contextual Embeddings","volume":"26","author":"Si","year":"2019","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"2022051016584927300_CIT0032","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/W19-5006","article-title":"Transfer Learning in Biomedical Natural Language Processing: An Evaluation of Bert and Elmo on Ten Benchmarking Datasets","author":"Peng","year":"2019"},{"key":"2022051016584927300_CIT0033","first-page":"1018","article-title":"An Information Retrieval Model Using the Fuzzy Proximity Degree of Term Occurences","author":"Beigbeder","year":"2005"},{"issue":"2","key":"2022051016584927300_CIT0034","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1109\/TKDE.2007.22","article-title":"An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval","volume":"19","author":"Castells","year":"2006","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"9","key":"2022051016584927300_CIT0035","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1016\/j.neucom.2011.12.057","article-title":"Ontology-Based Semantic Retrieval for Engineering Domain Knowledge","volume":"116","author":"Zhang","year":"2013","journal-title":"Neurocomputing"},{"issue":"8","key":"2022051016584927300_CIT0036","doi-asserted-by":"publisher","first-page":"2383","DOI":"10.1080\/00207543.2014.965352","article-title":"A Framework for Developing Engineering Design Ontologies Within the Aerospace Industry","volume":"53","author":"Sanya","year":"2015","journal-title":"Int. J. Prod. Res."},{"issue":"23","key":"2022051016584927300_CIT0037","doi-asserted-by":"publisher","first-page":"7187","DOI":"10.1080\/00207543.2017.1351643","article-title":"Graph-Based Knowledge Reuse for Supporting Knowledge-Driven Decision-Making in New Product Development","volume":"55","author":"Zhang","year":"2017","journal-title":"Int. J. Prod. Res."},{"issue":"11","key":"2022051016584927300_CIT0038","doi-asserted-by":"publisher","first-page":"111402","DOI":"10.1115\/1.4037649","article-title":"A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval","volume":"139","author":"Shi","year":"2017","journal-title":"ASME J. Mech. Des."},{"issue":"12","key":"2022051016584927300_CIT0039","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1016\/j.eswa.2018.07.017","article-title":"OpenIE-Based Approach for Knowledge Graph Construction From Text","volume":"113","author":"Martinez-Rodriguez","year":"2018","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"2022051016584927300_CIT0040","doi-asserted-by":"publisher","first-page":"112995","DOI":"10.1016\/j.eswa.2019.112995","article-title":"TechNet: Technology Semantic Network Based on Patent Data","volume":"142","author":"Sarica","year":"2020","journal-title":"Expert Syst. Appl."},{"issue":"7","key":"2022051016584927300_CIT0041","doi-asserted-by":"publisher","first-page":"1043","DOI":"10.1017\/pds.2021.104","article-title":"Design Knowledge Representation With Technology Semantic Network","volume":"1","author":"Sarica","year":"2021","journal-title":"Proc. Des. Soc."},{"issue":"2","key":"2022051016584927300_CIT0042","doi-asserted-by":"publisher","first-page":"021008","DOI":"10.1115\/1.4052293","article-title":"Engineering Knowledge Graph From Patent Database","volume":"22","author":"Siddharth","year":"2021","journal-title":"ASME J. Comput. Inf. Sci. Eng."},{"issue":"9","key":"2022051016584927300_CIT0043","doi-asserted-by":"publisher","first-page":"091102","DOI":"10.1115\/1.4042793","article-title":"Mining Changes of User Expectations Over Time From Online Reviews","volume":"141","author":"Hou","year":"2019","journal-title":"ASME J. Mech. Des."},{"key":"2022051016584927300_CIT0044","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1016\/j.procir.2020.02.190","article-title":"A Computational Approach for Using Social Networking Platforms to Support Creative Idea Generation","volume":"91","author":"Han","year":"2020","journal-title":"Procedia CIRP"},{"issue":"6","key":"2022051016584927300_CIT0045","doi-asserted-by":"publisher","first-page":"061403","DOI":"10.1115\/1.4048819","article-title":"Eliciting Attribute-Level User Needs From Online Reviews With Deep Language Models and Information Extraction","volume":"143","author":"Han","year":"2020","journal-title":"ASME J. Mech. Des."},{"key":"2022051016584927300_CIT0046","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.procir.2021.05.005","article-title":"Extracting Functional Requirements From Design Documentation Using Machine Learning","volume":"100","author":"Akay","year":"2021","journal-title":"Procedia CIRP"},{"issue":"3","key":"2022051016584927300_CIT0047","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10845-021-01749-4","article-title":"Similarity-Based Approach for Inventive Design Solutions Assistance","volume":"32","author":"Ni","year":"2021","journal-title":"J. Intell. Manuf."},{"issue":"1","key":"2022051016584927300_CIT0048","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-016-9475-9","article-title":"Recent Automatic Text Summarization Techniques: A Survey","volume":"47","author":"Gambhir","year":"2017","journal-title":"Artif. Intell. Rev."},{"issue":"4","key":"2022051016584927300_CIT0049","first-page":"14","article-title":"Review of Automatic Text Summarization Techniques & Methods","volume":"34","author":"Widyassari","year":"2020","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"2022051016584927300_CIT0050","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1075\/nlp.3","volume-title":"Automatic Summarization","author":"Mani","year":"2001"},{"key":"2022051016584927300_CIT0051","article-title":"Bleu: A Method for Automatic Evaluation of Machine Translation","author":"Papineni","year":"2002"},{"key":"2022051016584927300_CIT0052","article-title":"Rouge: A Package for Automatic Evaluation of Summaries","author":"Lin","year":"2004"},{"key":"2022051016584927300_CIT0053","first-page":"376","article-title":"Meteor Universal: Language Specific Translation Evaluation for any Target Language","author":"Denkowski","year":"2014"},{"key":"2022051016584927300_CIT0054","doi-asserted-by":"crossref","DOI":"10.3115\/1118108.1118117","article-title":"NLTK: The Natural Language Toolkit","author":"Loper","year":"2002"},{"key":"2022051016584927300_CIT0055","article-title":"Adam: A Method for Stochastic Optimization","author":"Kingma","year":"2015"},{"key":"2022051016584927300_CIT0056","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/D19-1410","article-title":"Sentence-Bert: Sentence Embeddings Using Siamese Bert-Networks","author":"Reimers","year":"2019"},{"key":"2022051016584927300_CIT0057","first-page":"91","volume-title":"ICML","author":"Bradley","year":"1998"},{"key":"2022051016584927300_CIT0058","article-title":"Exploring Content Models for Multi-Document Summarization","author":"Aria","year":"2009"},{"key":"2022051016584927300_CIT0059","article-title":"Textrank: Bringing Order Into Text","author":"Mihalcea","year":"2004"},{"issue":"4","key":"2022051016584927300_CIT0060","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1177\/0165551511408848","article-title":"Text Summarization Using Latent Semantic Analysis","volume":"37","author":"Ozsoy","year":"2011","journal-title":"J. Inf. Sci."},{"key":"2022051016584927300_CIT0061","article-title":"Looking for a Few Good Metrics: Automatic Summarization Evaluation-how Many Samples are Enough?","author":"Lin","year":"2003"},{"key":"2022051016584927300_CIT0062","doi-asserted-by":"crossref","DOI":"10.14569\/IJACSA.2017.081052","article-title":"Text Summarization Techniques: A Brief Survey","volume-title":"CoRR","author":"Allahyari","year":"2017"},{"issue":"3","key":"2022051016584927300_CIT0063","doi-asserted-by":"publisher","first-page":"258","DOI":"10.4304\/jetwi.2.3.258-268","article-title":"A Survey of Text Summarization Extractive Techniques","volume":"2","author":"Gupta","year":"2010","journal-title":"J. Emerg. Technol. Web Intell."},{"key":"2022051016584927300_CIT0064","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/E17-2007","article-title":"The Limits of Automatic Summarisation According to Rouge","author":"Schluter","year":"2017"},{"key":"2022051016584927300_CIT0065","doi-asserted-by":"crossref","DOI":"10.3115\/1557690.1557747","article-title":"Correlation Between Rouge and Human Evaluation of Extractive Meeting Summaries","author":"Liu","year":"2008"},{"key":"2022051016584927300_CIT0066","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/D19-1006","article-title":"How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings","author":"Kawin Ethayarajh","year":"2019"},{"key":"2022051016584927300_CIT0067","doi-asserted-by":"crossref","DOI":"10.1109\/NICS51282.2020.9335899","article-title":"Fine-Tuning BERT for Sentiment Analysis of Vietnamese Reviews","author":"Nguyen","year":"2020"},{"key":"2022051016584927300_CIT0068","article-title":"Universal sentence encoder","author":"Cer","year":"2018"},{"key":"2022051016584927300_CIT0069","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/D17-1070","article-title":"Supervised Learning of Universal Sentence Representations From Natural Language Inference Data","author":"Conneau","year":"2017"},{"issue":"11","key":"2022051016584927300_CIT0070","first-page":"1","article-title":"Visualizing Data Using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["Journal of Computing and Information Science in Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/asmedigitalcollection.asme.org\/computingengineering\/article-pdf\/22\/6\/061004\/6878322\/jcise_22_6_061004.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/asmedigitalcollection.asme.org\/computingengineering\/article-pdf\/22\/6\/061004\/6878322\/jcise_22_6_061004.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T17:19:29Z","timestamp":1675099169000},"score":1,"resource":{"primary":{"URL":"https:\/\/asmedigitalcollection.asme.org\/computingengineering\/article\/22\/6\/061004\/1139824\/Engineering-Document-Summarization-A-Bidirectional"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,10]]},"references-count":70,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,12,1]]}},"URL":"https:\/\/doi.org\/10.1115\/1.4054203","relation":{},"ISSN":["1530-9827","1944-7078"],"issn-type":[{"value":"1530-9827","type":"print"},{"value":"1944-7078","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,10]]},"article-number":"061004"}}