Neural data-to-text systems lack the control and factual accuracy required to generate useful and insightful summaries of multidimensional data. We propose a solution in the form of data views, where each view describes an entity and its attributes along specific dimensions. A sequence of views can then be used as a high-level schema for document planning, with the neural model handling the complexities of micro-planning and surface realization. We show that our view-based system retains factual accuracy while offering high-level control of output that can be tailored based on user preference or other norms within the domain.

We report error analysis of outputs from seven Table-to-Text generation models fine-tuned on ToTTo, an open-domain English language dataset. A manual error annotation of a subset of outputs (a total of 5,278 sentences) belonging to the topic of Politics generated by these seven models has been carried out. Our error annotation focused on eight categories of errors. The error analysis shows that more than 45% of sentences from each of the seven models have been error-free. It uncovered some of the specific classes of errors such as WORD errors that are the dominant errors in all the seven models, NAME and NUMBER errors are more committed by two of the GeM benchmark models, whereas DATE-DIMENSION and OTHER category of errors are more common in our Table-to-Text models.

It is unfair to expect neural data-to-text to produce high quality output when there are gaps between system input data and information contained in the training text. Thomson et al. (2020) identify and narrow information gaps in Rotowire, a popular data-to-text dataset. In this paper, we describe a study which finds that a state-of-the-art neural data-to-text system produces higher quality output, according to the information extraction (IE) based metrics, when additional input data is carefully selected from this newly available source. It remains to be shown, however, whether IE metrics used in this study correlate well with humans in judging text quality.

This paper presents a study to understand the issues related to using NLG to humanise explanations from a popular interpretable machine learning framework called LIME. Our study shows that self-reported rating of NLG explanation was higher than that for a non-NLG explanation. However, when tested for comprehension, the results were not as clear-cut showing the need for performing more studies to uncover the factors responsible for high-quality NLG explanations.

This paper proposes an approach to NLG system design which focuses on generating output text which can be more easily processed by the reader. Ways in which cognitive theory might be combined with existing NLG techniques are discussed and two simple experiments in content ordering are presented.

Many data-to-text NLG systems work with data sets which are incomplete, ie some of the data is missing. We have worked with data journalists to understand how they describe incomplete data, and are building NLG algorithms based on these insights. A pilot evaluation showed mixed results, and highlighted several areas where we need to improve our system.

Notre société génère une masse d’information toujours croissante, que ce soit en médecine, en météorologie, etc. La méthode la plus employée pour analyser ces données est de les résumer sous forme graphique. Cependant, il a été démontré qu’un résumé textuel est aussi un mode de présentation efficace. L’objectif du prototype BT-45, développé dans le cadre du projet Babytalk, est de générer des résumés de 45 minutes de signaux physiologiques continus et d’événements temporels discrets en unité néonatale de soins intensifs (NICU). L’article présente l’aspect génération de texte de ce prototype. Une expérimentation clinique a montré que les résumés humains améliorent la prise de décision par rapport à l’approche graphique, tandis que les textes de BT-45 donnent des résultats similaires à l’approche graphique. Une analyse a identifié certaines des limitations de BT-45 mais en dépit de cellesci, notre travail montre qu’il est possible de produire automatiquement des résumés textuels efficaces de données complexes.