  For (semi-)automated subject indexing systems in digital libraries, it is
often more practical to use metadata such as the title of a publication instead
of the full-text or the abstract. Therefore, it is desirable to have good text
mining and text classification algorithms that operate well already on the
title of a publication. So far, the classification performance on titles is not
competitive with the performance on the full-texts if the same number of
training samples is used for training. However, it is much easier to obtain
title data in large quantities and to use it for training than full-text data.
In this paper, we investigate the question how models obtained from training on
increasing amounts of title training data compare to models from training on a
constant number of full-texts. We evaluate this question on a large-scale
dataset from the medical domain (PubMed) and from economics (EconBiz). In these
datasets, the titles and annotations of millions of publications are available,
and they outnumber the available full-texts by a factor of 20 and 15,
respectively. To exploit these large amounts of data to their full potential,
we develop three strong deep learning classifiers and evaluate their
performance on the two datasets. The results are promising. On the EconBiz
dataset, all three classifiers outperform their full-text counterparts by a
large margin. The best title-based classifier outperforms the best full-text
method by 9.4%. On the PubMed dataset, the best title-based method almost
reaches the performance of the best full-text classifier, with a difference of
only 2.9%.
