  Over the last decade, the use of Deep Learning in many applications produced
results that are comparable to and in some cases surpassing human expert
performance. The application domains include diagnosing diseases, finance,
agriculture, search engines, robot vision, and many others. In this paper, we
are proposing an architecture that utilizes image/video captioning methods and
Natural Language Processing systems to generate a title and a concise abstract
for a video. Such a system can potentially be utilized in many application
domains, including, the cinema industry, video search engines, security
surveillance, video databases/warehouses, data centers, and others. The
proposed system functions and operates as followed: it reads a video;
representative image frames are identified and selected; the image frames are
captioned; NLP is applied to all generated captions together with text
summarization; and finally, a title and an abstract are generated for the
video. All functions are performed automatically. Preliminary results are
provided in this paper using publicly available datasets. This paper is not
concerned about the efficiency of the system at the execution time. We hope to
be able to address execution efficiency issues in our subsequent publications.
