DataNarrative: Automated Data-Driven Storytelling  with Visualizations and Texts
Authors: Mohammed Saidul Islam, Enamul Hoque, Shafiq Joty, Md Tahmid Rahman Laskar, Md Rizwan Parvez


Reviewer paper summaries: In this paper, the authors propose generating data-driven storytelling with visualization and texts automatically.
Summary of strengths: - Wide experimentation setup;
- Interesting research that could interest some broad or narrow sub-communities in NLP;
Summary of weaknesses: - Organization of the presentation of information throughout the article.
Comments, suggestions and typos: The paper proposal is exciting because it addresses a relevant research topic. Due to its quality and merits, I only have a few issues that I would like to bring to light:
- The figures are presented too far from their reference in the text, which generates a break in the reading and understanding every time one is cited.
- Multiple references must be done in chronological order. It should be (Zhou et al., 2023; Xie and Riedl, 2024;) instead of (Xie and Riedl, 2024; Zhou et al., 2023), for instance.
Some other (not exhaustive) issues that the authors should review:
- There are some recent surveys related to the narrative extraction task that also highlights the task addressed in this paper. A reference to it would help reinforce the theoretical basis and keep it up to date.
Overall assessment scores: 4