Bibliographical metadata collections describing pre-modern objects suffer from incompleteness and inaccuracies. This hampers the identification of literary works. In addition, titles often contain voluminous descriptive texts that do not adhere to contemporary title conventions. This paper explores several NLP approaches where greater textual length in titles is leveraged to enhance descriptive information.

In this paper, we present a crowdsourced dataset which adds entity salience (importance) annotations to the Reuters-128 dataset, which is subset of Reuters-21578. The dataset is distributed under a free license and publish in the NLP Interchange Format, which fosters interoperability and re-use. We show the potential of the dataset on the task of learning an entity salience classifier and report on the results from several experiments.