Extracting abstract and keywords from context for academic articles

Müngen, Ahmet Anıl; Kaya, Mehmet

doi:10.1007/s13278-018-0524-z

Cited by 13 publications

(3 citation statements)

References 23 publications

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“…Unlike the reviewer and proposal representation methods used in other studies, Li and Chen (2011) developed the similarity calculation between proposals and reviewers based on knowledge set theory. Müngen and Kaya (2018) believed that looking at the background information and metadata of reviewer candidates' research is insufficient to search for reviewers. Therefore, they introduced a new statistical approach called Topical Affinity Propagation (II-TAP), in which the reviewer's expertise was determined based on various features, such as the reviewer's papers, the metadata of these papers, the reviewer's changing interests over time and the reviewer's co-authors' expertise.…”

Section: Other Semantic-based Methodsmentioning

confidence: 99%

Reviewer Assignment Problem: A Systematic Review of the Literature

Meltem¹,

Yanık

Amasyalı

2023

jair

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Appropriate reviewer assignment significantly impacts the quality of proposal evaluation, as accurate and fair reviews are contingent on their assignment to relevant reviewers. The crucial task of assigning reviewers to submitted proposals is the starting point of the review process and is also known as the reviewer assignment problem (RAP). Due to the obvious restrictions of manual assignment, journal editors, conference organizers, and grant managers demand automatic reviewer assignment approaches. Many studies have proposed assignment solutions in response to the demand for automated procedures since 1992. The primary objective of this survey paper is to provide scholars and practitioners with a comprehensive overview of available research on the RAP. To achieve this goal, this article presents an in-depth systematic review of 103 publications in the field of reviewer assignment published in the past three decades and available in the Web of Science, Scopus, ScienceDirect, Google Scholar, and Semantic Scholar databases. This review paper classified and discussed the RAP approaches into two broad categories and numerous subcategories based on their underlying techniques. Furthermore, potential future research directions for each category are presented. This survey shows that the research on the RAP is becoming more significant and that more effort is required to develop new approaches and a framework.

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Section: Other Semantic-based Methodsmentioning

confidence: 99%

Reviewer Assignment Problem: A Systematic Review of the Literature

Meltem¹,

Yanık

Amasyalı

2023

jair

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“…TurKeyX [17] çalışmasından daha başarılı sonuçlar elde edilmiş, KEA-TR ile ise yakın başarım göstermiştir. Başka bir çalışmada metin verisi olarak akademik yayınları kullanarak ASÇ gerçekleştirilmiştir [19]. Doğal Dil İşleme (DDİ) yöntemleri ile öncelikle ilgeçleri ve bağlaçları metinlerden çıkarmışlardır.…”

Section: Kaynak Araştırmasıunclassified

Türkçe Anahtar Sözcük Çıkarımında LSTM ve BERT Tabanlı Modellerin Karşılaştırılması

Aydın,

Kantarcı

2024

Bilgisayar Bilimleri Ve Mühendisliği Dergisi

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Günümüzde internet ortamında metne dayalı veri çok hızlı bir şekilde artış göstermektedir ve bu büyük veri içinden istenilen bilgiyi barındıran doğru içeriklere ulaşabilmek önemli bir ihtiyaçtır. İçeriklere ait anahtar sözcüklerin bilinmesi bu ihtiyacı karşılamada olumlu bir etki sağlayabilmektedir. Bu çalışmada, doğal dil işleme ve derin öğrenme modelleri ile Türkçe metinleri temsil eden anahtar sözcüklerin belirlenmesi amaçlanmıştır. Veri kümesi olarak Türkçe Etiketli Metin Derlemi ve Metin Özetleme-Anahtar Kelime Çıkarma Veri Kümesi birlikte kullanılmıştır. Derin öğrenme modeli olarak çalışmada iki farklı model ortaya konmuştur. İlk olarak Uzun Ömürlü Kısa Dönem Belleği ( LSTM) katmanlı bir Diziden Diziye (Seq2Seq) model tasarlanmıştır. Diğer model ise BERT (Transformatörler ile İki Yönlü Kodlayıcı Temsilleri) ile oluşturulmuş Seq2Seq bir modeldir. LSTM katmanlı Seq2seq modelin başarı değerlendirmesinde ROUGE-1 ölçütünde 0,38 F-1 değerine ulaşılmıştır. BERT tabanlı Seq2Seq modelde ROUGE-1 ölçütünde 0,399 F-1 değeri elde edilmiştir. Sonuç olarak dönüştürücü mimarisini temel alan BERT tabanlı Seq2Seq modelin, LSTM tabanlı Seq2seq modele görece daha başarılı olduğu gözlemlenmiştir.

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“…Similar publications are often found by measuring the similarity of the keywords describing the publication or the similarity of the part of the publication, such as the summary, with other publications [20,21]. In other words, similar publications can be found with the common word number between two publications.…”

Section: Introductionmentioning

confidence: 99%

Personalised publication recommendation service for open-access digital archives

Müngen

2022

Journal of Information Science

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Increase in the number of open-access academic publications and open-access institutional academic archives led more researchers use these archives. No model offers personalised publication suggestions in academic archives. A central service architecture has been proposed towards personalised academic article recommendations for open-access digital archives. Thus, it has been possible to make personalised suggestions for open-access digital archives and enable researchers to discover new publications. A service based on the centralised micro-service architecture model was proposed in the study. Also, TF-IDF and article classification methods were used together for the personalised publication suggestion system. For the first time globally, the proposed method was used with 1464 real users in 49 open-access archives. F-measure success value was found to be higher than 0.8 for recommended publications.

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Extracting abstract and keywords from context for academic articles

Cited by 13 publications

References 23 publications

Reviewer Assignment Problem: A Systematic Review of the Literature

Reviewer Assignment Problem: A Systematic Review of the Literature

Türkçe Anahtar Sözcük Çıkarımında LSTM ve BERT Tabanlı Modellerin Karşılaştırılması

Personalised publication recommendation service for open-access digital archives

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