---
title: "Graph-Augmented Code Summarization in Computational Notebooks"
authors:
  - "April Wang"
  - "Dakuo Wang"
  - "Xuye Liu"
  - "Lingfei Wu"
doi: "10.24963/ijcai.2021/717"
url: "https://ijcai.org/proceedings/2021/717"
pdf: "https://ijcai.org/proceedings/2021/0717.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "5020-5023"
track: "Demo Track"
keywords:
  - "Human-Computer Interaction: General"
  - "Natural Language Processing: General"
---

# Graph-Augmented Code Summarization in Computational Notebooks

April Wang, Dakuo Wang, Xuye Liu, Lingfei Wu

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 5020-5023. DOI: [10.24963/ijcai.2021/717](https://doi.org/10.24963/ijcai.2021/717)

## Abstract

Computational notebooks allow data scientists to express their ideas through a combination of code and documentation. However, data scientists often pay attention only to the code and neglect the creation of the documentation in a notebook. In this work, we present a human-centered automation system, Themisto, that can support users to easily create documentation via three approaches: 1) We have developed and reported a GNN-augmented code documentation generation algorithm in a previous paper, which can generate documentation for a given source code; 2) Themisto also implements a query-based approach to retrieve the online API documentation as the summary for certain types of source code; 3) Lastly, Themistoalso enables a user prompt approach to motivate users to write documentation for some use cases that automation does not work well.