# Process-To-Text: A Framework for the Quantitative Description of Processes in Natural Language

arXiv: https://arxiv.org/abs/2305.14044 · HTML: https://mlsanity.com/paper/2305.14044/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 23 May 2023.

## Builds on (most cited first, 4 of 4)

- [Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation](https://mlsanity.com/paper/1703.09902/) (2017)
- [Automated Discovery of Process Models from Event Logs: Review and Benchmark](https://mlsanity.com/paper/1705.02288/) (2017)
- [Fuzzy Sets Across the Natural Language Generation Pipeline](https://mlsanity.com/paper/1605.05303/) (2016)
- [Mining Frequent Patterns in Process Models](https://mlsanity.com/paper/1710.05693/) (2017)

## Abstract (arXiv)

In this paper we present the Process-To-Text (P2T) framework for the automatic generation of textual descriptive explanations of processes. P2T integrates three AI paradigms: process mining for extracting temporal and structural information from a process, fuzzy linguistic protoforms for modelling uncertain terms, and natural language generation for building the explanations. A real use-case in the cardiology domain is presented, showing the potential of P2T for providing natural language explanations addressed to specialists.