Understanding users’ reading behaviour can facilitate and support the development of data lexicalisation models based on users’ characteristics. A significant amount of data can be found online in tabular form, and several models have been developed to provide the user with summaries of the content of such tables. Nevertheless, studies analysing table reading patterns are almost entirely lacking in the literature, making it almost impossible to integrate findings on user reading behaviour into lexicalisation models. This work aims to suggest a new line of gaze-related research that can integrate insights about user behaviour and characteristics into data summarisation algorithms to provide textual content that meets the user’s information needs. An overview of human-gaze studies applied to natural language will be presented to outline a study on human interaction with tables. Potential fields of application and challenges in applying these results to the field of table summarisation, namely the task of producing short summaries of tabular data, from a user-centred perspective will be discussed.
Amianto Barbato, J., Cremaschi, M. (2022). Bridging the gap between human-gaze data and table summarisation. In Proceedings of the 1st Workshop on Artificial Intelligence for Human Machine Interaction 2022 co-located with the 21st International Conference of the Italian Association for Artificial Intelligence (AIxIA 2022) (pp.1-8). CEUR-WS.
Bridging the gap between human-gaze data and table summarisation
Amianto Barbato, J;Cremaschi, M
2022
Abstract
Understanding users’ reading behaviour can facilitate and support the development of data lexicalisation models based on users’ characteristics. A significant amount of data can be found online in tabular form, and several models have been developed to provide the user with summaries of the content of such tables. Nevertheless, studies analysing table reading patterns are almost entirely lacking in the literature, making it almost impossible to integrate findings on user reading behaviour into lexicalisation models. This work aims to suggest a new line of gaze-related research that can integrate insights about user behaviour and characteristics into data summarisation algorithms to provide textual content that meets the user’s information needs. An overview of human-gaze studies applied to natural language will be presented to outline a study on human interaction with tables. Potential fields of application and challenges in applying these results to the field of table summarisation, namely the task of producing short summaries of tabular data, from a user-centred perspective will be discussed.
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