###### Abstract
The combination of graphics and text is an effective explanation strategy [33] because the users can focus their attention on the explainer information in the text [34]. At the same time, graphics allows users to form mental models of the information explained in the text. The combination of visual and text information is a useful explanation strategy when it meets four conditions [35]: (1) the text is easy for all users to understand; (2) the visuals are created and evaluated based on the user's comprehension; (3) the visual representations are employed to provide users with good explanations of the texts; and (4) the experience of users with the content is little or no previous. Additionally, video is a ubiquitous data type not only for viewing (reading) but for daily communication and composition (writing) [36]. For all the above considerations, we propose Automatic Video Generation (AVG) as a new interactive NL technology for Explainable AI whose objective is to automatically generate step-by-step explainer videos to improve our understanding of complex phenomena dealing with a large amount of information, HNNs in our case. The idea is to combine in the same framework explanations in NL with visual representations of the data structures and algorithms employed in a process of machine learning and put them together in a sequence of frames (or slides). The result is a video presentation similar to those created by teachers, experts, or researchers when they need to explain something to an audience. Our approach is strongly context-dependent; hence, we will use this feature to provide users with factual and realistic explanations. Moreover, storytelling techniques help us to include global commentaries. Additionally, as we have videos (mp4, slides, ppt) as output, the user can classically analyze them: forward, backward, pause, slow, fast, so on. Another significant advantage is that a complete understanding of the models could require several examples. Our approach can generate several videos from different samples quickly using a small number of changes. As this is a first approximation, we have some limitations; we highlight two: (i) the difficulty of generating videos with large neural networks; (ii) the limited degree of sophistication of the sentences generated in natural language. These limitations do not diminish the fact that our proposal is promising, and both limitations will address in future works. It is important to highlight, although we employ our video-generation method for explaining the execution of a HNN, it could be applied to any problem whose solution involves the implementation of a certain algorithm (classical or machine learning ones) because we use the execution traces to automatically obtain the sequence of frames that will compose the future explainer video. More formally, our research (hypothesis, main objective, methodology, result and evaluation) is explained and formalized in Figures 1 and 2. Additionally, our proposed method is presented and explained in Figures 3 and 4 using block diagrams [37]. In the literature, we can find several works about automatic video generation which differ quite a lot from our proposal [38; 39]. In [38] a framework for machine learning to generate videos called ArrowGAN is presented and in [39] a multi-modal approach for automatically generating hierarchical tutorials from instructional makeup videos is  
Figure 1: A general framework for transforming the execution of an algorithm in an explainer video using data-to-text and software visualization techniques. Figure 2: Hypothesis, Objective, Methodology, Result and Evaluation. proposed. The main difference is that in our case videos are automatically generated from execution traces (symbolic information) to obtain a better understanding of machine learning algorithms. In the mentioned approaches videos are employed to generating other videos with different objectives. On the other hand, there is another line of work which aims to automatically generate textual explanations of software projects (code) [40].