Improving Scientific Article Visibility by Neural Title Simplification
This addresses the challenge for scientific content providers in making articles more visible to users through simplified titles, though it appears incremental as it builds on existing neural models with added post-processing.
The paper tackled the problem of improving scientific article visibility by automatically generating titles with varying informativeness to attract different user categories, achieving a trade-off between attractiveness and transparency in recommendations using neural sequence-to-sequence models.
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.