Semi-automatic System for Title Construction

TL;DR

This paper presents a semi-automatic system that extracts impactful keywords from scientific abstracts to assist authors in constructing effective titles, leveraging a supervised keyword extraction model based on graph-text features.

Contribution

The work introduces a novel graph-based feature set for keyword extraction that is domain-independent and improves title construction from scientific abstracts.

Findings

Achieved 82% macro-averaged precision in keyword overlap evaluation.

Demonstrated the effectiveness of graph-based features for keyword discrimination.

Validated the approach across diverse text domains.

Abstract

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%.