| Vol-3604 urn:nbn:de:0074-3604-0 Copyright © 2023 for the individual papers by the papers' authors. Copyright © 2023 for the volume as a collection by its editors. This volume and its papers are published under the Creative Commons License Attribution 4.0 International (CC BY 4.0).

# PatentSemTech 2023
Patent Text Mining and Semantic Technologies (PatentSemTech) 2023

### Proceedings of the 4th Workshop on Patent Text Mining and Semantic Technologies (PatentSemTech) 2023
co-located with the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2023)

### Taipeh, Taiwan, July 27th, 2023.

Edited by

### Hidir Aras *
Ralf Krestel **
Linda Andersson ***
Florina Piroi ****
Allan Hanbury *****
Dean Alderucci ******

* FIZ Karlsruhe - Leibniz Institute for Information Infrastructure , Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
** ZBW - Leibniz Information Centre for Economics & Kiel University, Düsternbrooker Weg 120, 24105 Kiel, Germany
*** Artificial Researcher IT GmbH, Taubstummengasse 11 (i2c), 1040, Wien Austria
**** RSA FG Studio Data Science, Thurngasse 8/16, 1090, Vienna Austria
***** Institute of Information Systems Engineering, TU Wien,, Favoritenstr. 9-11/194-04, Vienna, Austria
****** Center for AI and Patent Analysis, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA

## Table of Contents

- Preface
  Summary: There were 10 papers submitted for peer-review to this workshop. Out of these, 7 papers were accepted for this volume, 2 as regular papers and 5 as short papers.

### Regular Papers

- Benchmarking Natural Language Processing Algorithms for Patent Summarization 1-11
  Silvia Casola, Alberto Lavelli
- Explainable Artificial Intelligence for Highlighting and Searching in Patent Text 12-21
  Renukswamy Chikkamath, Rana Fassahat Ali, Christoph Hewel, Markus Endres

### Short Papers

- Automatic Generation of Explanatory Text from Flowchart Images in Patents 22-26
  Hidetsugu Nanba, Shohei Kubo, Satoshi Fukuda
- Ensemble Method for Classification in Imbalanced Patent Data 27-32
  Eleni Kamateri, Michail Salampasis
- Patent Classification on Search-Optimized Graph-Based Representations 33-38
  Jarkko Lagus, Ekaterina Kotliarova, Sebastian Björkqvist
- Exploring the Impact of Negative Sampling on Patent Citation Recommendation 39-43
  Rima Dessi, Hidir Aras, Mehwish Alam
- A Patent Semantic Representation Using Technical Compound Sentences 44-49
  Shuxuan Xiang, Jin Mao, Gang Li

2023-11-29: submitted by Ralf Krestel, metadata incl. bibliographic data published under Creative Commons CC0
2023-01-01: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) |valid HTML5|