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| "paper_id": "U19-1016", |
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| "date_generated": "2023-01-19T03:07:42.306229Z" |
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| "authors": [ |
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| "first": "Connor", |
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| "last": "Stead", |
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| "institution": "Macquarie University", |
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| "country": "Australia" |
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| "email": "connor.stead@hdr.mq.edu.au" |
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| { |
| "first": "Stephen", |
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| "last": "Smith", |
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| "institution": "Macquarie University", |
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| "email": "stephen.smith@mq.edu.au" |
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| { |
| "first": "Peter", |
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| "last": "Busch", |
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| "institution": "Macquarie University", |
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| "email": "peter.busch@mq.edu.au" |
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| { |
| "first": "Savanid", |
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| "last": "Vatanasakdakul", |
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| "institution": "Carnegie Mellon University", |
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| "country": "Qatar" |
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| "email": "savanid@cmu.edu" |
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| "abstract": "Background: Datasets available for abstract sentence classification modelling are predominately comprised of abstracts sourced from biomedical research. Aims: To contribute a large non-biomedical multidisciplinary dataset for abstract sentence classification model research. Method: Bulk extract and transformation of Emerald Group Publishing structured abstracts indexed on Scopus. Results: We present the largest multidisciplinary dataset for abstract sentence classification modelling, consisting of 1,050,397 sentences from 103,457 abstracts.", |
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| "text": "Background: Datasets available for abstract sentence classification modelling are predominately comprised of abstracts sourced from biomedical research. Aims: To contribute a large non-biomedical multidisciplinary dataset for abstract sentence classification model research. Method: Bulk extract and transformation of Emerald Group Publishing structured abstracts indexed on Scopus. Results: We present the largest multidisciplinary dataset for abstract sentence classification modelling, consisting of 1,050,397 sentences from 103,457 abstracts.", |
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| "text": "Abstracts enable researchers to efficiently determine the relevance of literature to their research (Rowley, 1982 , Collision, 1971 , Cleveland and Cleveland, 2013 . The desire to optimise this efficiency has resulted in the adoption of structured abstracts, which feature explicit headings reflecting key characteristics of a study. Examples of these headings include: aim, method, results and contributions. The alternative to structured abstracts are those where sentences addressing such characteristics are not specified.", |
| "cite_spans": [ |
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| "start": 100, |
| "end": 113, |
| "text": "(Rowley, 1982", |
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| "start": 114, |
| "end": 131, |
| "text": ", Collision, 1971", |
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| "start": 132, |
| "end": 163, |
| "text": ", Cleveland and Cleveland, 2013", |
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| "section": "Introduction", |
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| "text": "Compared to unstructured alternatives, structured abstracts are perceived to offer greater value for researchers (Sharma and Harrison, 2006 , Taddio et al., 1994 and Guimar\u00e3es, 2006 ; permit advanced access to research findings (Mosteller et al., 2004) , contain more relevant information (Budgen et al., 2008) and are easier to read (Kitchenham et al., 2008 and Budgen et al., 2008) . Structured abstracts also increase the likelihood that relevant research is discovered (Eldredge, 2006 , Mulrow, 1987 , Haynes et al., 1990 , Hartley, 1997 , Bayley et al., 2002 and Bayley and Eldredge, 2003 .", |
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| "text": "(Sharma and Harrison, 2006", |
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| "text": ", Taddio et al., 1994", |
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| "end": 181, |
| "text": "and Guimar\u00e3es, 2006", |
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| "text": "(Mosteller et al., 2004)", |
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| "text": "(Budgen et al., 2008)", |
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| "text": "Budgen et al., 2008)", |
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| "text": "(Eldredge, 2006", |
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| "text": ", Mulrow, 1987", |
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| "text": ", Hartley, 1997", |
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| "text": "and Bayley and Eldredge, 2003", |
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| "section": "Introduction", |
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| "text": "Natural language processing (NLP) has been used to automate the structuring of unstructured abstracts (Gon\u00e7alves et al., 2018; Jin and Szolovits, 2018, Dernoncourt et al., 2016) ; which is achieved through the development of Abstract Sentence Classification Models (ASCM), capable of classifying sentences sourced from unstructured abstracts into structured abstract headings.", |
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| "text": "(Gon\u00e7alves et al., 2018;", |
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| "start": 127, |
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| "text": "Jin and", |
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| "text": "Szolovits, 2018, Dernoncourt et al., 2016)", |
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| "section": "Introduction", |
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| "text": "This paper presents a novel dataset to advance ASCM research. The dataset introduced is unlike those already leveraged in ASCM development, primarily as it is comprised of abstracts originating from disciplines not yet explored in current research. The adoption of our dataset in future model development will enable the benchmarking of ASCM capability in new disciplines.", |
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| "section": "Introduction", |
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| "text": "There are numerous datasets available to researchers seeking to develop ASCM. These are outlined in table 1, an extension of the table presented by Dernoncourt and Lee (2017, p. 3) . We extend their table by identifying the abstract's disciplinary domain. The size represents the number of abstracts reflected in the dataset. The 'manual' flag identifies if sentences were manually classified into structured abstract headings by the authors (Y) or were pre-structured in the original abstract (N).", |
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| "text": "Dernoncourt and Lee (2017, p. 3)", |
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| "text": "table 1, an extension of the table presented by", |
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| "section": "Related Work", |
| "sec_num": "2" |
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| "text": "The number of datasets available for ASCM does not directly correspond to the number of ASCM studies, as researchers re-use datasets to benchmark performance and to test novel algorithms. Further, studies may develop a dataset for model development without contributing the dataset as an artefact. Dernoncourt and Lee (2017) also presented a dataset in a standalone paper, Emerald 110k: A Multidisciplinary Dataset for Abstract Sentence Classification much like this body of work. Table 2 provides a summary of ASCM development efforts, along with the dataset used in model development. Hara et al. (2007) 200 Y BM (RCT) Hirohata et al. (2008) 104k N BM Chung (2009) 327 Y BM (RCT) Boudin et al. 2010 It is evident the dataset contributed by Kim et al. (2011) and Dernoncourt and Lee (2017) enjoys significant adoption in ASCM development. This dataset represents in practice the concern that the almost exclusive benefactor of advancements in ASCM studies are researchers in the biomedical discipline, and that the abstracts of non-biomedical disciplines have predominately not been included in model development.", |
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| "text": "Hara et al. (2007)", |
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| "text": "Hirohata et al. (2008)", |
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| "text": "There are a few studies representing exceptions to the biomedical exclusive trend. These are identified in table 2 with an asterisk (*). The first example is Teufel and Moens (1998) , who developed a Naive Bayes classifier using sentences retrieved from 201 computational linguistics and cognitive science abstracts, achieving 68.6% precision (p. 24). Further non-biomedical examples include Wu et al. (2006) who used the computer and information science academic index Citeseer as an abstract source and Liu et al. (2013) who used ScienceDirect, a primarily scientific and health science academic literature index. These datasets are not available for researcher utilisation.", |
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| "section": "Dataset Size Manual Domain", |
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| "text": "In response to the lack of disciplinary diversity, we are exploring greater non-biomedical grounded ASCM development. We desire to increase the likelihood that ASCM capability can become a viable inter-disciplinary mechanism to increase research discovery and accessibility. As part of our research, we have created a novel multidisciplinary abstract sentence dataset for future ASCM development. The dataset development process is outlined in the following section. 2003Study developed (Medline) Shimbo et al. (2003) Study developed (Medline) Ito et al. 2004Study developed (Medline) Yamamoto and Takagi (2005) Study developed (Medline) Wu et al. (2006) * Study developed (Citeseer) Lin et al. (2006) Study developed Xu et al. (2006) Study developed (RCT -source unknown) Ruch et al. 2007Study developed (Medline) Hirohata et al. 2008Study developed (Medline) Chung 2009Study developed (Medline) Kim et al. 2011Study developed (Medline) Lui 2012 ", |
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| "text": "We present a novel abstract sentence dataset for ASCM research. The dataset contains sentences retrieved from multi-disciplinary non-biomedical journal abstracts. Each sentence is classified as belonging to one of the following heading classes:", |
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| "section": "Dataset Development", |
| "sec_num": "3" |
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| "text": "\u2022 Purpose", |
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| "section": "Dataset Development", |
| "sec_num": "3" |
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| "text": "\u2022 Design/methodology/approach \u2022 Findings \u2022 Originality/value \u2022 Social implications \u2022 Practical implications \u2022 Research limitations/implications", |
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| "section": "Dataset Development", |
| "sec_num": "3" |
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| "text": "As of 2019, Emerald Group Publishing (henceforth: Emerald) publishes over 300 doubleblind peer reviewed journals (Emerald Group Publishing, 2019). Emerald journals publish research from management, information science and engineering disciplines. This includes fields such as aerospace technology, management information systems, corporate governance, marketing, computing, accounting, public health, supply chain management and tourism (Emerald Group Publishing, 2019).", |
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| "section": "Abstract Identification", |
| "sec_num": "3.1" |
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| "text": "In 2005 Emerald began mandating the use of structured abstracts in their journal publications (Emerald Group Publishing Limited, 2005) . The multidisciplinary nature of Emerald's journal portfolio combined with their mandated structured abstract adoption policy has resulted in a unique opportunity for ASCM development. However, existing ASCM research has failed to leverage Emerald journal abstracts for model development.", |
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| "section": "Abstract Identification", |
| "sec_num": "3.1" |
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| "text": "The Scopus academic literature index was utilised to obtain Emerald journal abstracts. This was due to the availability of an API to access Scopus content, as well as the reach and scope of the index. An initial examination of Scopus identified 336 Emerald journals available where research was published between 2005 and 2019. This count indicated that the Emerald portfolio was widely available through Scopus.", |
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| "section": "Abstract Extract", |
| "sec_num": "3.2" |
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| "text": "After determining the availability of Emerald journals on Scopus, we developed a Python program capable of autonomously querying Scopus for Emerald journal records, downloading results and storing them on a local machine. This was made possible by Elsevier's Scopus API (https://dev.elsevier.com/) and the Python package Pybliometrics (https://github.com/pybliometricsdev/pybliometrics).", |
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| "section": "Abstract Extract", |
| "sec_num": "3.2" |
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| "text": "The program processed a CSV file containing a list of Emerald journal ISSN codes. The program iterated over each observation in the CSV, querying Scopus for all publications from the journal between 2004 and 2019. The year 2004 was chosen as it was possible that some journals adopted structured abstracts prior to 2005, the time in which Emerald mandated the use of structured abstracts across their publications (Emerald Group Publishing Limited, 2005) . The downloaded observations did not include the full text of the article, only metadata such as: DOI, article title, authors, publication date and the abstract.", |
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| "section": "Abstract Extract", |
| "sec_num": "3.2" |
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| "text": "There were 138,613 journal article metadata observations retrieved from the Scopus queries. These were exported into a Microsoft Excel workbook for manual unstructured/structured abstract classification. An abstract was deemed to be structured if it featured the Emerald structured abstract headings and these headings were used to separate components of what would otherwise have been free text abstracts. As a result, 109,608 abstracts were classified as structured, with the remaining abstracts discarded.", |
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| "section": "Abstract Extract", |
| "sec_num": "3.2" |
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| "text": "Existing datasets utilised in ASCM research are presented as sentence level observations, featuring a sentence string with its corresponding structured abstract class. To ensure easy adoption in model development, it was necessary to deconstruct the abstracts into sentences, whilst maintaining the structured abstract class they reflected.", |
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| "section": "Abstract Sentence Transformation", |
| "sec_num": "3.3" |
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| "text": "A program was developed which processed each abstract, identifying the locations of the structured headings and treating them as delimiters. This segmented the base abstract string into heading level substrings. We then used a tokenizer to split these into sentence strings, which were reviewed to identify data quality issues such as: sentences incorrectly split from the tokenizer (for example, seeing i.e. as an end of sentence condition), presence of a copyright indicator as the last sentence observation and invalid heading classes.", |
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| "section": "Abstract Sentence Transformation", |
| "sec_num": "3.3" |
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| "text": "Any data quality issues identified were managed either through sentence modification or removal of the base abstract; which ensured the dataset contained all sentences from base abstracts.", |
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| "section": "Abstract Sentence Transformation", |
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| "text": "Post sentence transformation, we formed a dataset consisting of 1,050,397 sentences originating from 103,457 abstracts. A heading level summary of the sentence abstract count is provided in table 3. Sentence per abstract and token per sentence frequency as well as descriptive statistics are provided in figures 1 and 2. We note the low frequency for the 'Social implications' class. Table 4 identifies the sentence and abstract counts for the top 15 (of 406) journals featuring abstracts. This demonstrates its multidisciplinary nature.", |
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| "section": "Resulting Dataset", |
| "sec_num": "3.4" |
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| "text": "We named our dataset Emerald 110k, following the ASCM dataset naming convention set by Dernoncourt and Lee (2017) ", |
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| "text": "This paper explored the development of a novel dataset for ASCM research. The novelty of this dataset is primarily due to its composition of abstract sentences from a range of non-biomedical disciplinary literature. Our dataset is also the second largest dataset available. It offers a unique opportunity for ASCM researchers to explore the performance of their models outside of biomedical abstract datasets.", |
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| "section": "Conclusion and Ongoing Research", |
| "sec_num": "4" |
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| "text": "Our future research is concerned with expanding ASCM outside of biomedicine and providing associated advancements to new disciplines. Accordingly, we are utilizing this dataset in our own exploration of state of the art ASCM development. We also intend to update this dataset as additional Emerald structured abstracts are published each year, whilst seeking to identify new sources of structured abstracts for ASCM research.", |
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| "section": "Conclusion and Ongoing Research", |
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| "text": "The authors wish to acknowledge the Australian Government Research Training Program Scholarship which enabled this research to take place.", |
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| "section": "Acknowledgements", |
| "sec_num": "5" |
| } |
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| "type_str": "table", |
| "num": null, |
| "content": "<table><tr><td>Heading</td><td colspan=\"2\">Sentences Abstracts</td></tr><tr><td>Purpose</td><td>198,277</td><td>103,394</td></tr><tr><td colspan=\"2\">Design/methodology/approach 223,312</td><td>101,328</td></tr><tr><td>Findings</td><td>269,321</td><td>103,268</td></tr><tr><td>Originality/value</td><td>187,986</td><td>102,559</td></tr><tr><td>Social implications</td><td>26</td><td>15</td></tr><tr><td>Practical implications</td><td>92,243</td><td>48,689</td></tr><tr><td>Research limitations</td><td>79,232</td><td>40,544</td></tr><tr><td>/implications</td><td/><td/></tr></table>" |
| }, |
| "TABREF4": { |
| "html": null, |
| "text": "", |
| "type_str": "table", |
| "num": null, |
| "content": "<table><tr><td>: Heading level summary of resulting dataset</td></tr><tr><td>Figure 1: Sentence per Abstract Frequency. Minimum:</td></tr><tr><td>1 Maximum: 60 Mean: 10.1530 Standard Deviation:</td></tr><tr><td>3.4253 Skewness: 1.2720 Kurtosis: 5.2848</td></tr><tr><td>Figure 2: Tokens per Sentence Frequency. Minimum: 1</td></tr><tr><td>Maximum: 309 Mean: 23.34 Standard Deviation:</td></tr><tr><td>10.4790 Skewness: 1.3217 Kurtosis: 5.5276</td></tr></table>" |
| }, |
| "TABREF5": { |
| "html": null, |
| "text": "Sentence and abstract frequency for the top 15 journals in the dataset (ordered by sentence count)", |
| "type_str": "table", |
| "num": null, |
| "content": "<table/>" |
| } |
| } |
| } |
| } |
