# Learning to Select, Track, and Generate for Data-to-Text

## TL;DR

This paper introduces a novel data-to-text generation model with tracking and generation modules that emulate human writing, improving summary quality by effectively selecting and organizing information.

## Contribution

The paper presents a new model with separate tracking and generation modules, demonstrating improved performance over existing methods and exploring the role of writer information.

## Key findings

- Outperforms existing models on all evaluation metrics.
- Incorporating writer information enhances generation quality.
- Effective information tracking improves content planning.

## Abstract

We propose a data-to-text generation model with two modules, one for tracking and the other for text generation. Our tracking module selects and keeps track of salient information and memorizes which record has been mentioned. Our generation module generates a summary conditioned on the state of tracking module. Our model is considered to simulate the human-like writing process that gradually selects the information by determining the intermediate variables while writing the summary. In addition, we also explore the effectiveness of the writer information for generation. Experimental results show that our model outperforms existing models in all evaluation metrics even without writer information. Incorporating writer information further improves the performance, contributing to content planning and surface realization.