# Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation

## TL;DR

This paper introduces a head-modifier template-based approach for generating accurate, readable type descriptions in knowledge graphs, addressing grammatical and factual issues present in previous methods.

## Contribution

The paper presents a novel head-modifier template method, a new dataset, and two automatic metrics for improving type description generation in knowledge graphs.

## Key findings

- Significant improvement over baselines in readability and data fidelity.
- Achieves state-of-the-art performance on new and existing datasets.
- Introduces a new dataset and evaluation metrics for the task.

## Abstract

A type description is a succinct noun compound which helps human and machines to quickly grasp the informative and distinctive information of an entity. Entities in most knowledge graphs (KGs) still lack such descriptions, thus calling for automatic methods to supplement such information. However, existing generative methods either overlook the grammatical structure or make factual mistakes in generated texts. To solve these problems, we propose a head-modifier template-based method to ensure the readability and data fidelity of generated type descriptions. We also propose a new dataset and two automatic metrics for this task. Experiments show that our method improves substantially compared with baselines and achieves state-of-the-art performance on both datasets.