Seoul National University of Science and Technology Introduces PV2DOC: A New Tool for Summarizing Presentation Videos Efficiently

In a world increasingly dominated by digital communication, presentation videos have emerged as a popular method for conveying information, particularly in academic settings. These videos, often rich with slides, figures, tables, and spoken commentary, have flourished in adoption, particularly following the COVID-19 pandemic when traditional face-to-face interactions were largely curtailed. However, while they serve as a compelling medium for dissemination, presentation videos pose significant challenges. One of the most pressing issues is that they can be prohibitively time-consuming; viewers often find themselves watching lengthy recordings in their entirety just to locate specific information. Additionally, their large file sizes present challenges regarding storage and ease of access.

Researchers at Seoul National University of Science and Technology, led by Professor Hyuk-Yoon Kwon, have recognized these shortcomings and developed an innovative software tool known as PV2DOC. This groundbreaking application is designed to transform the way users interact with presentation videos, effectively converting these unstructured audiovisual formats into highly organized and easily accessible documents. Unlike conventional video summarizers that rely on pre-existing transcripts, PV2DOC uniquely harnesses both visual and audio elements from the videos themselves, creating condensed documents that maintain the essential content.

The potential for PV2DOC to revolutionize the accessibility of information is vast. For students and professionals who frequently engage with multiple presentation videos—such as lectures or conference talks—the tool promises to generate summarized reports that condense the content into readable formats achievable within a matter of minutes. This means individuals can quickly glean relevant insights without having to sift through dense video material. Furthermore, PV2DOC treats figures and tables with special attention, managing these components separately and linking them to the corresponding summarized text, enhancing the user’s ability to reference essential details without losing context.

PV2DOC’s image processing capabilities are quite sophisticated. The tool extracts video frames at one-second intervals, employing a methodology known as the structural similarity index to detect unique frames by comparing them to preceding ones. In a practical sense, this means the software efficiently identifies key visuals without redundant repetitions. The next challenge is to analyze these frames for important objects, which is achieved using advanced object detection models such as Mask R-CNN and YOLOv5. Often, these images may contain disjointed elements due to whitespace or sub-figures. PV2DOC addresses this by implementing a figure merge technique, which amalgamates overlapping visuals into cohesive representations.

Further enhancing its functionality, the software also performs optical character recognition (OCR) through the Google Tesseract engine to extract any text present within the identified images. This text extraction is essential for converting visual data into structured written content, allowing PV2DOC to facilitate a seamless flow of information. The software organizes this extracted textual data into a coherent format, including elements like headings and paragraphs that are well-suited for reading and comprehension.

Alongside its image processing features, PV2DOC also efficiently manages audio data. The application extracts audio tracks from presentation videos and converts them into written text using the Whisper model, an open-source speech-to-text tool. This transcription process is pivotal for creating an accessible summary of the video’s main ideas and arguments. To create these summaries, PV2DOC employs the TextRank algorithm, which swiftly synthesizes the transcribed content into concise overviews. The result is a well-structured Markdown document that presents the extracted images and text collaboratively, mimicking the original format of the video while maximizing clarity and organization.

The use of PV2DOC not only dramatically enhances the accessibility of material contained in video presentations but also facilitates significant reductions in storage space. By transforming unstructured audiovisual data into structured text documents, the software paves the way for easier sharing, archiving, and analysis of video content. As Professor Kwon notes, this transformation serves dual purposes: improving information accessibility and optimizing data management. The ease with which users can navigate through summarized reports enables more efficient use of multimedia resources, setting a new standard for how academic and professional presentation content is disseminated and utilized.

Despite these substantial advances, the researchers at Seoul National University of Science and Technology are not stopping here. They have ambitious plans to further enhance PV2DOC, setting their sights on training a large language model (LLM) akin to ChatGPT. This next step envisions the development of a question-answering capability where users could pose specific inquiries related to the content extracted from presentation videos, and the model would respond with accurate, context-aware answers. Such an initiative would not only make previously recorded material more interactive but would also deepen the users’ engagement with the content.

As digital information continues to proliferate, the necessity for tools like PV2DOC becomes increasingly apparent. The ongoing evolution of this technology not only reflects a changing landscape in educational and professional environments but also highlights a growing recognition of the importance of making knowledge accessible. By facilitating quicker access to valuable information and minimizing unnecessary strain on storage resources, PV2DOC has the potential to reshape how we engage with and learn from presentation videos moving forward.

The groundbreaking work by Professor Hyuk-Yoon Kwon and his team signals a pivotal moment in the intersection of technology and education. As they forge ahead with refinements to their software, the anticipation mounted by both academic institutions and industry professionals alike will surely only continue to grow. In a world where information is key, PV2DOC stands at the forefront, striving to simplify, summarize, and ultimately enhance the ways we consume knowledge in the digital age.

Subject of Research: Not applicable
Article Title: PV2DOC: Converting the presentation video into the summarized document
News Publication Date: 1-Dec-2024
Web References: https://en.seoultech.ac.kr/
References: DOI: 10.1016/j.softx.2024.101922
Image Credits: Associate Professor Hyuk-Yoon Kwon, Seoul National University of Science and Technology

Keywords: Information accessibility, data management, presentation videos, software development, artificial intelligence, educational technology, audio processing, image processing, document conversion, structured data, video summarization.