# Improving Scientific Article Visibility by Neural Title Simplification

## TL;DR

This paper presents a neural title simplification method to generate varied article titles, enhancing scientific article visibility and tailoring titles for different user preferences to improve recommendation effectiveness.

## Contribution

It introduces a neural sequence-to-sequence approach with biasing and post-processing to produce diverse simplified titles for scientific articles.

## Key findings

- Achieved a trade-off between attractiveness and transparency of titles.
- Utilized biased training data and post-processing for title variety.
- Enhanced recommendation system effectiveness through title simplification.

## Abstract

The rapidly growing amount of data that scientific content providers should deliver to a user makes them create effective recommendation tools. A title of an article is often the only shown element to attract people's attention. We offer an approach to automatic generating titles with various levels of informativeness to benefit from different categories of users. Statistics from ResearchGate used to bias train datasets and specially designed post-processing step applied to neural sequence-to-sequence models allow reaching the desired variety of simplified titles to gain a trade-off between the attractiveness and transparency of recommendation.

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Source: https://tomesphere.com/paper/1904.03172