# Helping News Editors Write Better Headlines: A Recommender to Improve the Keyword Contents & Shareability of News Headlines

## TL;DR

This paper introduces a software tool that leverages NLP and machine learning to assist news editors in creating headlines with optimized keywords and shareability, enhancing online engagement and SEO.

## Contribution

The paper presents a novel system integrating keyword relevance ranking and shareability prediction to improve headline writing for online news.

## Key findings

- Effective identification of salient keywords in news articles.
- Accurate prediction of headline shareability on social media.
- User interface design streamlines headline composition process.

## Abstract

We present a software tool that employs state-of-the-art natural language processing (NLP) and machine learning techniques to help newspaper editors compose effective headlines for online publication. The system identifies the most salient keywords in a news article and ranks them based on both their overall popularity and their direct relevance to the article. The system also uses a supervised regression model to identify headlines that are likely to be widely shared on social media. The user interface is designed to simplify and speed the editor's decision process on the composition of the headline. As such, the tool provides an efficient way to combine the benefits of automated predictors of engagement and search-engine optimization (SEO) with human judgments of overall headline quality.