Elias Entrup

Number of publications: 6

2024

1. [1]

   E. Entrup, R. Ewerth, and A. Hoppe:

   Can Editorial Decisions Impair Journal Recommendations? Analysing the Impact of Journal Characteristics on Recommendation Systems

   In: ACM Conference on Recommender Systems, RecSys 2024, Bari, Italy, October 14-18, 2024, pp. 1062–1066. ACM, 2024.

   https://doi.org/10.1145/3640457.3688194

   Bib

           @inproceedings{DBLP:conf/recsys/EntrupEH24,
     author = {Entrup, Elias and Ewerth, Ralph and Hoppe, Anett},
     editor_ = {Tommaso Di Noia and
                       Pasquale Lops and
                       Thorsten Joachims and
                       Katrien Verbert and
                       Pablo Castells and
                       Zhenhua Dong and
                       Ben London},
     title = {Can Editorial Decisions Impair Journal Recommendations? Analysing the Impact of Journal Characteristics on Recommendation Systems},
     booktitle = {{ACM} Conference on Recommender Systems, RecSys 2024, Bari, Italy, October 14-18, 2024},
     pages = {1062--1066},
     publisher = {{ACM}},
     year = {2024},
     doi = {10.1145/3640457.3688194}
   }

2. [2]

   A. Brack, E. Entrup, M. Stamatakis, P. Buschermöhle, A. Hoppe, and R. Ewerth:

   Sequential sentence classification in research papers using cross-domain multi-task learning

   International Journal on Digital Libraries, 2024.

   https://doi.org/10.1007/s00799-023-00392-z

   Bib

   The automatic semantic structuring of scientific text allows for more efficient reading of research articles and is an important indexing step for academic search engines. Sequential sentence classification is an essential structuring task and targets the categorisation of sentences based on their content and context. However, the potential of transfer learning for sentence classification across different scientific domains and text types, such as full papers and abstracts, has not yet been explored in prior work. In this paper, we present a systematic analysis of transfer learning for scientific sequential sentence classification. For this purpose, we derive seven research questions and present several contributions to address them: (1) We suggest a novel uniform deep learning architecture and multi-task learning for cross-domain sequential sentence classification in scientific text. (2) We tailor two transfer learning methods to deal with the given task, namely sequential transfer learning and multi-task learning. (3) We compare the results of the two best models using qualitative examples in a case study. (4) We provide an approach for the semi-automatic identification of semantically related classes across annotation schemes and analyse the results for four annotation schemes. The clusters and underlying semantic vectors are validated using k-means clustering. (5) Our comprehensive experimental results indicate that when using the proposed multi-task learning architecture, models trained on datasets from different scientific domains benefit from one another. Our approach significantly outperforms state of the art on full paper datasets while being on par for datasets consisting of abstracts.

           @article{Brack2024,
     author = {Brack, Arthur and Entrup, Elias and Stamatakis, Markos and Buschermöhle, Pascal and Hoppe, Anett and Ewerth, Ralph},
     journal = {International Journal on Digital Libraries},
     title = {Sequential sentence classification in research papers using cross-domain multi-task learning},
     year = {2024},
     issn = {1432-1300},
     doi = {10.1007/s00799-023-00392-z},
     refid = {Brack2024},
     url_ = {https://doi.org/10.1007/s00799-023-00392-z}
   }

2023

1. [1]

   E. Entrup, R. Ewerth, and A. Hoppe:

   A Comparison of Automated Journal Recommender Systems

   In: International Conference on Theory and Practice of Digital Libraries, TPDL 2023, Zadar, Croatia, September 26-29, 2023, pp. 230–238. Springer, 2023.

   https://doi.org/10.1007/978-3-031-43849-3_20

   Bib

           @inproceedings{DBLP:conf/ercimdl/EntrupEH23,
     author = {Entrup, Elias and Ewerth, Ralph and Hoppe, Anett},
     booktitle = {International Conference on Theory and Practice of Digital Libraries, {TPDL} 2023, Zadar, Croatia, September 26-29, 2023},
     title = {A Comparison of Automated Journal Recommender Systems},
     year = {2023},
     pages = {230--238},
     publisher = {Springer},
     bibsource = {dblp computer science bibliography, https://dblp.org},
     biburl = {https://dblp.org/rec/conf/ercimdl/EntrupEH23.bib},
     doi = {10.1007/978-3-031-43849-3\_20},
     editor_ = {Omar Alonso and Helena Cousijn and Gianmaria Silvello and M{\'{o}}nica Marrero and Carla Teixeira Lopes and Stefano Marchesin},
     series_ = {Lecture Notes in Computer Science},
     timestamp = {Sun, 17 Dec 2023 00:00:00 +0100},
     url_ = {https://doi.org/10.1007/978-3-031-43849-3\_20},
     volume_ = {14241}
   }

2. [2]

   E. Entrup, A. Eppelin, R. Ewerth, J. Hartwig, M. Tullney, M. Wohlgemuth, and A. Hoppe:

   Comparing different search methods for the open access journal recommendation tool B!SON

   International Journal on Digital Libraries, 2023.

   https://doi.org/10.1007/s00799-023-00372-3

   Bib

   Finding a suitable open access journal to publish academic work is a complex task: Researchers have to navigate a constantly growing number of journals, institutional agreements with publishers, funders’ conditions and the risk of predatory publishers. To help with these challenges, we introduce a web-based journal recommendation system called B!SON. A systematic requirements analysis was conducted in the form of a survey. The developed tool suggests open access journals based on title, abstract and references provided by the user. The recommendations are built on open data, publisher-independent and work across domains and languages. Transparency is provided by its open source nature, an open application programming interface (API) and by specifying which matches the shown recommendations are based on. The recommendation quality has been evaluated using two different evaluation techniques, including several new recommendation methods. We were able to improve the results from our previous paper with a pre-trained transformer model. The beta version of the tool received positive feedback from the community and in several test sessions. We developed a recommendation system for open access journals to help researchers find a suitable journal. The open tool has been extensively tested, and we found possible improvements for our current recommendation technique. Development by two German academic libraries ensures the longevity and sustainability of the system.

           @article{Entrup2023,
     author = {Entrup, Elias and Eppelin, Anita and Ewerth, Ralph and Hartwig, Josephine and Tullney, Marco and Wohlgemuth, Michael and Hoppe, Anett},
     journal = {International Journal on Digital Libraries},
     title = {Comparing different search methods for the open access journal recommendation tool B!SON},
     year = {2023},
     issn = {1432-1300},
     doi = {10.1007/s00799-023-00372-3},
     refid = {Entrup2023},
     url_ = {https://doi.org/10.1007/s00799-023-00372-3}
   }

2022

1. [1]

   E. Entrup, A. Eppelin, R. Ewerth, J. Hartwig, M. Tullney, M. Wohlgemuth, and A. Hoppe:

   B!SON: A Tool for Open Access Journal Recommendation

   In: International Conference on Theory and Practice of Digital Libraries, TPDL 2022, Padua, Italy, September 20-23, 2022, pp. 357–364. Springer, 2022.

   https://doi.org/10.1007/978-3-031-16802-4_33

   Bib

           @inproceedings{DBLP:conf/ercimdl/EntrupEEHTWH21,
     author = {Entrup, Elias and Eppelin, Anita and Ewerth, Ralph and Hartwig, Josephine and Tullney, Marco and Wohlgemuth, Michael and Hoppe, Anett},
     title = {B!SON: {A} Tool for Open Access Journal Recommendation},
     booktitle = {International Conference on Theory and Practice of Digital Libraries, {TPDL} 2022, Padua, Italy, September 20-23, 2022},
     pages = {357--364},
     publisher = {Springer},
     year = {2022},
     doi = {10.1007/978-3-031-16802-4\_33},
     timestamp = {Thu, 05 Jan 2023 17:08:54 +0100},
     biburl = {https://dblp.org/rec/conf/ercimdl/EntrupEEHTWH21.bib},
     bibsource = {dblp computer science bibliography, https://dblp.org}
   }

2021

1. [1]

   A. Eppelin, E. Entrup, J. Hartwig, and A. Hoppe:

   B!SON–a tool to make OA journal selection easier

   In: Septentrio Conference Series(4), 2021.

   Bib

           @inproceedings{Eppelin2021,
     author = {Eppelin, Anita and Entrup, Elias and Hartwig, Josephine and Hoppe, Anett},
     booktitle = {Septentrio Conference Series},
     title = {B!SON--a tool to make OA journal selection easier},
     year = {2021},
     number = {4}
   }