Visual data stories shown in the form of narrative visualizations such as a poster or a data
video, are frequently used in data-oriented storytelling to facilitate the understanding and …

Data-to-text Generation (D2T) aims to generate textual natural language statements that can
fluently and precisely describe the structured data such as graphs, tables, and meaning …

Extracting underlying data from rasterized charts is tedious and inaccurate; values might be
partially occluded or hard to distinguish, and the quality of the image limits the precision of …

Recent neural models for data-to-text generation are mostly based on data-driven end-to-
end training over encoder-decoder networks. Even though the generated texts are mostly …

How to generate descriptions from structured data organized in tables? Existing approaches
using neural encoder-decoder models often suffer from lacking diversity. We claim that an …

Table2Text systems generate textual output based on structured data utilizing machine
learning. These systems are essential for fluent natural language interfaces in tools such as …

Quantitative information plays an important part in the financial and data analysis areas.
Prior work relied on pattern-matching methods and complex hand-crafted rules to extract …

The comprehensive descriptions for factual attribute-value tables, which should be accurate,
informative and loyal, can be very helpful for end users to understand the structured data in …

We present a system for automatically extracting pertinent medical information from
dialogues between clinicians and patients. The system parses each dialogue and extracts …

Heterogenity of data-to-text generation datasets limits the research on data-to-text
generation systems. We present TabGenie-a toolkit which enables researchers to explore …