Streamlining Video Summarization with NLP: Techniques, Implementation, and Future Direction

The rapid growth of digital video content presents significant challenges in information accessibility and consumption, creating a pressing need for efficient video summarization techniques. This paper explores using natural language processing (NLP) to enhance video summarization by leveraging associated textual data such as subtitles, titles, and descriptions. Video summarization can be broadly divided into two main approaches: abstractive and extractive. We focus on extractive summarization, implementing various NLP techniques, including Pure NLTK, TextRank, LexRank, KL-Sum, and Naïve Reduction, each encapsulated in dedicated pipelines to tokenize, rank, and extract essential content. Subsequently, summaries are generated and presented as shortened video formats. Our methodology includes comparing these NLP-based techniques with current state-of-the-art approaches and evaluating them through various quantitative metrics such as Rouge, BLEU, and F1-score to ensure a comprehensive summarization quality and efficiency assessment. The study also addresses computational trade-offs, particularly focusing on optimizing summarization for real-time applications. Recognizing the reliance on textual data, we propose enhancements for handling videos with limited text, such as integrating audio-to-text conversion and visual analysis. Our findings underscore the potential of NLP-driven summarization in improving accessibility across varied video content types and provide a roadmap for future research to enhance scalability, personalization, and real-time applicability. The evaluation results demonstrate the effectiveness and potential of NLP techniques in video summarization regardless of video length, accent, or language. The findings highlight the effect of various NLP techniques on the form of the generated summaries. In addition, a comparison with state-of-the-art methodologies is performed to provide clearer insights into the quality of the summaries, not only regarding application quality but also regarding existing use cases. The study concludes by discussing the implications of the research findings and application tests. Finally, we propose future directions for video summarization enhancement and personalization.