2d:null 2e:["$","title",null,{"children":"Abstract Summary with MobileBERT qna | Michael Deagen | Observable"}] 33:T5d7,# Abstract Summary with MobileBERT qna An abstract of a research article provides a brief snapshot of the underlying work by highlighting the investigative approach and key findings. A well-written abstract should engage the reader while appropriately framing the research. Can we automate the retrieval of snippets of information from an abstract, given some common questions that a reader might have while reading it? This notebook aims to illustrate the use of a pre-trained natural language processing (NLP) model based on data pulled from a knowledge graph database and supplemented with information from an API for scholarly metadata. Given the DOI of an article related to materials science, this notebook fetches the abstract with the [SemanticScholar API](https://api.semanticscholar.org/) and uses the [pre-trained TensorFlow.js MobileBERT qna model](https://github.com/tensorflow/tfjs-models/tree/master/qna) to answer questions about the abstract. If the model finds a potential answer, the section of the passage containing the answer is returned along with a confidence score. Best of all, [everything is run within the browser](https://blog.tensorflow.org/2020/03/exploring-helpful-uses-for-bert-in-your-browser-tensorflow-js.html). The table below contains research articles about polymer nanocomposites whose data have been curated to MaterialsMine. Use the search bar to filter among articles, and **select a row in the table** to isolate an article for question answering.34:[] 31:{"id":"87713d78712e653b","slug":"abstract-summary-with-mobilebert-qna","title":"Abstract Summary with MobileBERT qna","thumbnail":"a65f202d754bacc9ef8905129a58a1b29f5b7983d420acaac8f1e71b5f00f897","access_level":"public","create_time":"$D2021-06-06T19:20:27.032Z","update_time":"$D2021-07-23T13:27:55.210Z","view_time":null,"first_public_time":"$D2021-06-06T22:04:56.540Z","has_unpublished_edits":null,"fork":false,"stars":2,"starred":false,"owner":{"type":"team","id":"5ca8a3812ea4a326","login":"mdeagen","name":"Michael Deagen","avatar_url":"https://avatars-next.observableusercontent.com/0b8e876e2eac1058faa6c1a2624346f3e72350b8a7e6c9acaec6a9ad920b0476","tier":"starter_2024"},"version":685,"publish_version":685,"role":null,"authored":false,"authors":[{"type":"individual","id":"d3611c0325cb99e9","login":"mdeagen","name":"Michael Deagen","avatar_url":"https://avatars-next.observableusercontent.com/0b8e876e2eac1058faa6c1a2624346f3e72350b8a7e6c9acaec6a9ad920b0476","tier":null}],"members":[{"type":"individual","id":"d3611c0325cb99e9","login":"mdeagen","name":"Michael Deagen","avatar_url":"https://avatars-next.observableusercontent.com/0b8e876e2eac1058faa6c1a2624346f3e72350b8a7e6c9acaec6a9ad920b0476","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":2,"collection":null,"type":"notebook","body":{"title":"Abstract Summary with MobileBERT qna","cells":[{"id":0,"value":"$33","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":27,"value":"viewof search = Search(MM_articles, {placeholder: \"Search article titles, materials, journals...\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":29,"value":"viewof articles = Table(search, {multiple: false, columns: [\"Authors\",\"Title\", \"Journal\"]})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":53,"value":"### Abstract of the selected article (fetched from SemanticScholar)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":80,"value":"viewof passage = Inputs.textarea({value: SemScholar_response.abstract, width: \"100%\", rows:8, submit: true, spellcheck: false})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":95,"value":"##### (Select a row from the table to display the text area, which is editable!)\n\n---\n\n## Question Answering\n\nTry it out by submitting your own question...","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":266,"value":"viewof q10 = Inputs.textarea({ placeholder: \"Type in your question here!\", value: \"What were the key results/findings?\", width:\"100%\", rows: 1, spellcheck: true, submit: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":512,"value":"{\n if(a10.length > 0) {\n return html`

${passage.substring(0,a10[0].startIndex) + \"\" + passage.substring(a10[0].startIndex,a10[0].endIndex+1) + \"\" + passage.substring(a10[0].endIndex+1,passage.length)}

`\n }\n else{\n return html``\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":180,"value":"Score: **${a10[0].score.toLocaleString(undefined, {maximumFractionDigits: 3})}**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":453,"value":"{\n if(a10.length == 0){\n return md`##### (Note: An error means that no satisfiable answers were returned)`\n }else{\n return md``\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":284,"value":"---\n\n...or browse the responses to various pre-defined questions below. \n\n---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":463,"value":"{\n if(a1.length > 0) {\n return md`\n**${q1}**\n \n*\"${a1[0].text}\"* \n##### (score: ${a1[0].score.toLocaleString(undefined, {maximumFractionDigits: 3})})`\n }\n else {return html``}\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":468,"value":"{\n if(a2.length > 0) {\n return md`\n**${q2}**\n \n*\"${a2[0].text}\"* \n##### (score: ${a2[0].score.toLocaleString(undefined, {maximumFractionDigits: 3})})`\n }\n else {return html``}\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":472,"value":"{\n if(a3.length > 0) {\n return md`\n**${q3}**\n \n*\"${a3[0].text}\"* \n##### (score: ${a3[0].score.toLocaleString(undefined, {maximumFractionDigits: 3})})`\n }\n else {return html``}\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":474,"value":"{\n if(a4.length > 0) {\n return md`\n**${q4}**\n \n*\"${a4[0].text}\"* \n##### (score: ${a4[0].score.toLocaleString(undefined, {maximumFractionDigits: 3})})`\n }\n else {return html``}\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":476,"value":"{\n if(a5.length > 0) {\n return md`\n**${q5}**\n \n*\"${a5[0].text}\"* \n##### (score: ${a5[0].score.toLocaleString(undefined, {maximumFractionDigits: 3})})`\n }\n else {return html``}\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":478,"value":"{\n if(a6.length > 0) {\n return md`\n**${q6}**\n \n*\"${a6[0].text}\"* \n##### (score: ${a6[0].score.toLocaleString(undefined, {maximumFractionDigits: 3})})`\n }\n else {return html``}\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":480,"value":"{\n if(a7.length > 0) {\n return md`\n**${q7}**\n \n*\"${a7[0].text}\"* \n##### (score: ${a7[0].score.toLocaleString(undefined, {maximumFractionDigits: 3})})`\n }\n else {return html``}\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":482,"value":"{\n if(a8.length > 0) {\n return md`\n**${q8}**\n \n*\"${a8[0].text}\"* \n##### (score: ${a8[0].score.toLocaleString(undefined, {maximumFractionDigits: 3})})`\n }\n else {return html``}\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":484,"value":"{\n if(a9.length > 0) {\n return md`\n**${q9}**\n \n*\"${a9[0].text}\"* \n##### (score: ${a9[0].score.toLocaleString(undefined, {maximumFractionDigits: 3})})`\n }\n else {return html``}\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":13,"value":"---\n## Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":118,"value":"q1 = `How were materials synthesized?`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":140,"value":"q2 = `How were samples prepared?`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":102,"value":"q3 = `Which materials were studied?`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":210,"value":"q4 = `What was improved?`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":212,"value":"q5 = `How were samples characterized?`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":229,"value":"q6 = `What is the application of interest?`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":252,"value":"q7 = `What properties were measured?`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":253,"value":"q8 = `What was evaluated?`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":255,"value":"q9 = `What were the insights?`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":121,"value":"a1 = model.findAnswers(q1,passage)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":104,"value":"a2 = model.findAnswers(q2,passage)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":153,"value":"a3 = model.findAnswers(q3,passage)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":214,"value":"a4 = model.findAnswers(q4,passage)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":215,"value":"a5 = model.findAnswers(q5,passage)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":232,"value":"a6 = model.findAnswers(q6,passage)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":233,"value":"a7 = model.findAnswers(q7,passage)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":235,"value":"a8 = model.findAnswers(q8,passage)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":237,"value":"a9 = model.findAnswers(q9,passage)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":239,"value":"a10 = model.findAnswers(q10,passage)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":20,"value":"import { querySPARQLendpoint } from '@mdeagen/sparql-select-query-function'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":22,"value":"import { Table, Search } from \"@observablehq/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":75,"value":"model = await qna.load()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":72,"value":"qna = require('@tensorflow-models/qna')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":48,"value":"SemScholar_response = (await fetch(SemScholar_query)).json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":46,"value":"SemScholar_query = `https://api.semanticscholar.org/v1/paper/` + selected_DOI","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":31,"value":"selected_DOI = articles.DOI","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":24,"value":"MM_articles = querySPARQLendpoint(SPARQL_endpoint, SPARQL_query)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":17,"value":"SPARQL_endpoint = \"https://materialsmine.org/wi/sparql\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":15,"value":"SPARQL_query = `PREFIX sio: \nPREFIX nm: \nPREFIX dct: \nPREFIX bibo: \nPREFIX rdf: \nPREFIX foaf: \nPREFIX rdfs: \nSELECT DISTINCT (MIN(?AuthorLabel) AS ?Authors) ?Title (REPLACE(STR(?doi),\"http://dx.doi.org/\",\"\") AS ?DOI) ?Journal\n(GROUP_CONCAT(DISTINCT ?Material; SEPARATOR=\"; \") AS ?Materials)\nWHERE {\n ?doi a dct:BibliographicResource ;\n dct:created ?Year ;\n dct:title ?Title ;\n dct:isPartOf [ dct:title ?Journal ] ;\n sio:hasPart ?sample ;\n bibo:authorList/rdf:rest* [ rdf:first [ foaf:name ?firstAuthor ] ; rdf:rest rdf:nil ] .\n ?sample a nm:PolymerNanocomposite ;\n sio:hasComponentPart [ a [ rdfs:label ?Material ] ]\n BIND(CONCAT(STR(?firstAuthor), \" et al. (\", STR(?Year), \")\") AS ?AuthorLabel )\n} GROUP BY ?doi ?Title ?Journal`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W34","stdlib":"1","titling":"auto"}} 32:"$31:body"