Performance Comparison of Ad-hoc Retrieval Models over Full-text vs. Titles of Documents

Citation

Saleh A, Beck T, Galke L & Scherp A (2018) Performance Comparison of Ad-hoc Retrieval Models over Full-text vs. Titles of Documents. In: Dobreva M, Hinze A & Žumer M (eds.) Maturity and Innovation in Digital Libraries. ICADL 2018. Lecture Notes in Computer Science, 11279. The 20th International Conference on Asia-Pacific Digital Libraries, Hamilton, New Zealand, 19.11.2018-22.11.2018. Cham, Switzerland: Springer, pp. 290-303. https://doi.org/10.1007/978-3-030-04257-8

Abstract
While there are many studies on information retrieval models using full-text, there are presently no comparison studies of full-text retrieval vs. retrieval only over the titles of documents. On the one hand, the full-text of documents like scientific papers is not always available due to, e. g., copyright policies of academic publishers. On the other hand, conducting a search based on titles alone has strong limitations. Titles are short and therefore may not contain enough information to yield satisfactory search results. In this paper, we compare different retrieval models regarding their search performance on the full-text vs. only titles of documents. We use different datasets, including the three digital library datasets: EconBiz, IREON, and PubMed. The results show that it is possible to build effective title-based retrieval models that provide competitive results comparable to full-text retrieval. The difference between the average evaluation results of the best title-based retrieval models is only % less than those of the best full-text-based retrieval models.

Keywords
Information retrieval; Learning to rank; Deep Learning;