Semi-automatic System for Title Construction
This addresses the challenge for researchers in efficiently generating manuscript titles, though it is incremental as it builds on existing keyword extraction methods.
The authors tackled the problem of constructing titles from scientific abstracts by developing a semi-automatic system that extracts and recommends impactful words, achieving a macro-averaged precision of 82% in keyword-title overlap.
In this paper, we propose a semi-automatic system for title construction from scientific abstracts. The system extracts and recommends impactful words from the text, which the author can creatively use to construct an appropriate title for the manuscript. The work is based on the hypothesis that keywords are good candidates for title construction. We extract important words from the document by inducing a supervised keyword extraction model. The model is trained on novel features extracted from graph-of-text representation of the document. We empirically show that these graph-based features are capable of discriminating keywords from non-keywords. We further establish empirically that the proposed approach can be applied to any text irrespective of the training domain and corpus. We evaluate the proposed system by computing the overlap between extracted keywords and the list of title-words for documents, and we observe a macro-averaged precision of 82%.