Department of Computer Science and Engineering, Ballari. Institute of Technology and Management, Ballari, India
Video has become one of the most prevalent methods of information dissemination and learning in today's digital world. However, manually viewing long videos to extract key points is very time-consuming and hence inefficient. In this project, we plan to develop a Video-to-Text Summarizer that automatically converts video content-whether from YouTube links or local files-into concise, meaningful summaries. The system will extract the audio from the video and use an Automatic Speech Recognition model to produce text. Further, using appropriate Natural Language Processing (NLP) techniques in conjunction with the state-of-the-art summarization algorithms-like transformer-based models such as BERT or GPT-the obtained transcript will be summarized. It thus helps users comprehend the core content of lengthy videos without having to go through every second of those videos. The proposed system can be applied in various domains like education, research, media analysis, and corporate training by making the consumption of content highly effective and accessible.
The video has emerged as one of the most popular and efficient modes of communication, education, and entertainment in the last few years. Millions of videos have been hosted on YouTube, Coursera, and TED, among others, that contain invaluable information in diverse domains. However, watching long videos to get important insights is really impractical and time-consuming for users who intend to attain quick comprehension. Video summarization poses a challenge in developing an automated system that summarizes video contents into text. The Video-to-Text Summarizer is devised to solve this problem by transforming the video data into short meaningful summaries. It takes either a YouTube video link or a local video file as an input. It then extracts the audio part from that, uses ASR techniques to perform speech-to-text conversion, and creates a transcript of the spoken content. Further processing is done on the transcript with the use of methods from NLP and algorithms for text summarization, yielding a summary that is clear and coherent. The project combines various technologies to enhance summarization: machine learning and deep learning, audio signal processing, and transformer-based models like BERT or GPT. It allows the viewer to summarize the main events in a video without having to go through the full content. It can definitely be useful in education, analysis of media, and research, and at the corporate level-where one needs to consume information efficiently. In conclusion, the Video-to-Text Summarizer provides an intelligent and time-saving way of managing volumes of video content by making such access very productive and facilitating ease of information retrieval.
LITERATURE REVIEW
The video-to-text summarization domain is interdisciplinary, incorporating studies on speech recognition, natural language understanding, multimodal learning, and deep neural summarization methods. Early works focused on creating compact visual summaries or choosing a representative keyframe selection, whereas modern summarization frameworks have now started shifting to semantic, language-guided, and transformer-based approaches. Some of the key trends in this direction include multilingual and cross-lingual video summarization. Li and Chen presented a framework that introduces deep multimedia processing pipelines for understanding multilingual video content [1]. More recently, with the advent of language-conditioned models, it has been made clear how text prompts serve as powerful guides for video summarization and boost semantic alignment among segments of video and their textual descriptions [2]. Equally, hierarchical multimodal transformer-based methods have incrementally improved in jointly processing audio, visual, and textual information to generate more informative summaries [3]. Recent advances also indicate developments concerning both conditional and context-sensitive summarization. Conditional modeling methods, as suggested by Huang et al., improve the quality of summaries by learning the dependencies among video frames with respect to their contextual importance [4]. Further, causal-aware models such as the Causalainer model explore relationships within an event flow in a video for creating more explainable and coherent summaries [5]. As part of structural models, graph-based modeling achieved improvement in the semantic flow preservation by reconstructing graphs representing video scenes [6]. The encoder–decoder framework remains widely used because of its efficacy for sequence-to-sequence modeling. Meanwhile, channel-attention mechanisms have been tried to enhance feature selection within the encoder–decoder pipeline, further improving the latter's temporal feature extraction capability on summarization tasks [7], [9]. Improved algorithms of machine learning have been used in key-shot-based selection models to extract critical moments in videos [8]. The most important recent changes include the integrations of LLM and prompt-based architectures in video summarization. Several self-supervised, LLM-driven methods have shown their potential for reworking language models to underline semantic boundaries, textual coherence, and reduce large annotated datasets. Extending this further into instruction-driven video-to-text summarization, Hua et al. propose V2Xum-LLM: a cross-modal summarization model that ensures better alignment between the video signals and textual outputs by incorporating instruction tuning and temporal prompts. Complementary to these, multimodal knowledge-aware networks provide domain knowledge and contextual signals that promote the generation of good quality textual summaries. Most recently, zero-shot and prompt-driven approaches have been explored. Barbara and Maalouf proposed a zero-shot video-to-text summarization system that is driven by pure language prompts; thus, this illustrates even deeper improvements in the complete avoidance of explicit training on video summary datasets [13]. This indicates that LLMs are able to generalize across video domains, hence increasing the adaptability and scalability of summarization.
III. Proposed work
This work gives an insight into an intelligent video-to-text summarizer, which takes a video as input either from a YouTube link or directly from a local video file and provides short, meaningful textual summaries by wrapping all the modules of audio extraction, speech recognition, text processing, and summarization into one seamless, efficient workflow for the user.
A. Overview of the System
Various stages involved in the system are as follows:
1. Input Acquisition: The system takes in either a link to a YouTube video or a local video file as an input.
2. Audio Extraction: This involves segregation of the audio stream from the video by utilities such as FFmpeg.
3. Speech-to-Text: This feature uses any of several speech recognition models, including but not limited to Google Speech Recognition API, Whisper, and Vosk, to transcribe the extracted audio into text.
4. Text Preprocessing: This step cleans up the transcript, formats it, and removes noise, fillers, and irrelevant information from it.
5. It summarizes the text based on extractive approaches, such as TextRank, and abstractive approaches with BERT, T5, and GPT-based models.
6. Output Generation: Here, the summary gets displayed or downloaded in readable format, text, or PDF. B. Algorithms and Techniques Applied
• Audio Extraction: FFmpeg worked perfectly for video-to-audio format conversions. Speech Recognition: Speech recognition systems using deep learning technology, including but not limited to Whisper or Google Speech API, have been applied here to provide correct transcriptions. Summary: o Extractive summarization: TextRank, TF-IDF scoring Abstractive Summarization: Transformer-based models like BERT, T5, or GPT. Preprocessing of text: stop word removal, punctuation normalization, and tokenization with either NLTK or spacy. C. Benefits of the Proposed System It summarizes long videos in an automated and time-efficient manner. It supports two of the most common sources of videos: online-YouTube and offline-local. It generates contextually understandable, grammatically correct summaries with the help of advanced NLP models. • Allows access to a wider audience and makes content more accessible to the busy user or to users who have hearing impairments. D. Expected Outcome The system will summarize any given video source into a concise, precise, and contextually relevant summary. With this, users will be able to get the sense of major ideas and key information in a very long video clip within seconds, hence greatly increasing productivity and efficiency in learning.
Fig 2: paste video link in search bar
Fig 4: Summary in other language
Fig 6: Translate option
?. Implementation
The Video-to-Text Summarizer will be implemented using an integrated combination of audio processing, speech recognition, and natural language processing. Different steps in a sequential pipeline will be executed to convert the video input provided via a YouTube link or local video file into a concise textual summary. They are as follows:
1.Input Module
The system takes two kinds of input sources:
• YouTube Video Link: This is where the user provides the URL of the YouTube video. It will download the video using yt-dlp, a Python library that's able to efficiently extract the best available streams of video and audio.
Local File: A local video file uploaded by the user can be in the format of .mp4, .mkv, and .avi. It will be directly processed without downloading it.
2. Audio Extraction
In the video files, the audio is extracted to a mono-channel WAV format of standard sample rate, usually 16 kHz, by FFmpeg, a free software multimedia framework. This will optimize the audio for further speech recognition processing.
3. Speech-to-Text Conversion
Resulting audio is then transcribed into text using Automatic Speech Recognition or, in short form, ASR. In this project, OpenAI Whisper-a deep learning-based ASR system that is multilingual and can operate in noisy environments-is used. Whisper processes the audio and provides accurate textual transcripts of the spoken content. The audio of longer videos is split automatically into chunks, such as 10-minute segments, using the pydub library. Each chunk is separately transcribed, and all segments are joined to give the complete transcript.
4. Text Preprocessing
Before summarization, standard NLP preprocessing is done, further supported by cleaning the raw transcript. It includes removal of filler words like "uh," "um," "you know"; normalization of punctuation; and correction of whitespace. This will make sure that the text to be fed into the summarization model will be clean, structured, and meaningful.
5. Text Summarization Advanced Transformer-based summarization models have been used to summarize the cleaned transcript. Among the different choices, models such as BART (facebook/bart-large-cnn) or T5 (Text-to-Text Transfer Transformer) from the Hugging Face Transformers library have been used for this project. The systems divide the text into chunks in cases of very long transcripts, summarize each chunk separately, combine the summaries together, and then pass them through another summarization layer that gives a final coherent context-aware summary. This two-step summarization generates more consistency and readability in the output.
RESULTS AND DISCUSSION
This section provides an overview of the results of developing decentralized applications, considering performance, security features, and user-centric benefits while describing challenges in the development process.
A. Performance Appraisal
Different types of videos include educative lectures, news, interviews, and YouTube tutorials on which the proposed Video-to-Text Summarizer system was implemented and tested. Indeed, it successfully turned both YouTube links and video files stored on a local device into meaningful summaries. The results manifest how efficiently ASR and NLP techniques are in extracting information and summarizing it. These ranged from videos of different lengths and audio qualities to test the system for accuracy and overall performance. In the case of single speakers with clear audio in videos, the transcription accuracy by the Whisper ASR model was above 90%, returning coherent and contextually accurate transcripts. This figure fell slightly when there was background noise, multiple speakers, or overlapping dialogues in the video; even then, it returned understandable text after preprocessing. Integration of text cleaning and removal of filler words increased readability of the transcripts overall before summarization. This summarization module was quite effective in compressing large transcripts into short paragraphs without losing the main idea of the original video content. For 30-60-minute-long educational videos, it generated a summary about 10-15% of the original length of the transcript and highlighted only the important concepts without any redundant information. Due to transformer-based models such as BART and T5, summaries obtained were grammatically correct and semantically coherent. Two-tier summarization does indeed yield better performance compared to summarizing directly from the full transcript, particularly for longer videos. Performance-wise, the system was quite efficient at mid-range hardware configurations. In fact, a 15-minute video took about 3-5 minutes to process with the Whisper-small model and BART summarizer using a GPU-enabled system. With such a modular design, one would feel free to replace the models with lighter or faster versions, depending on resource availability. Besides, the summaries were qualitatively tested against manually written summaries, and they shared a good deal of key points and thematic content. Further discussion enumerates some of the limitations observed during experimentation. Performance has considerable dependency on audio clarity and quality of speech recognition. In scenarios where there are multiple speakers, dialogue misattribution or skipping minor utterances was at times made by the ASR. Similarly, videos involving technical jargon and foreign accents often needed fine-tuning of the language model to perform better. Such limitations notwithstanding, the system offers a substantive time-saving advantage in that users need not go through long videos but rather have an overview of the essential information without necessarily viewing them.
CONCLUSIONS
This Video-to-Text Summarizer effectively shows how speech recognition and different NLP techniques can be combined to automatically summarize information from video content. It converts not just YouTube videos but also locally stored video files into readable and summarized textual format. The proposed model converts long-duration videos into short, meaningful summaries with much preservation of the essence of context and information through its modules related to the extraction of audio, speech-to-text conversion, text preprocessing, and summarization. Experiments reveal that this combination of OpenAI Whisper-transcribed text with a summarizer like BART or T5 increases coherence and accuracy to a high degree. The summarizer is then able to summarize very long transcripts into compact, contextually rich paragraphs that have proven highly useful for education, research, media analysis, and corporate communications. This system considerably reduces time taken by the users to comprehend video content and hence increases productivity and the accessibility of the users who need quick insights from lengthy materials. Although effective, there were certain limitations in this implementation. Transcription accuracy decreases if there is background noise, multiple speakers, or a strong accent present. Long videos with high technical or domain-specific vocabulary also require fine-tuned models for superior summarization performance. Those, in fact, are ways toward the further development of the system. There are promising ways this project might be extended further in the future. Real-time summarization will let the system summarize live lectures, webinars, and meetings. Support for various languages opens several avenues toward the usability of the system across languages and regions. Speaker identification and emotion detection could also be further included in building context around summaries, especially during interviews or discussions. Further development may also be done on the GUI or web-based dashboard, which allows nontechnical users to upload videos and see results. Integration with cloud platforms and API-based deployment will also scale the system for enterprise use or for educational purposes.
REFERENCE
Srisailanath, M. Manjunath*, M. Shashank, C. Sharath Vamshi, Sai Gagan Tej K. B., A Unified Video Content Understanding Framework for Youtube and Local Videos with Multilingual Summarization Support, Int. J. Sci. R. Tech., 2026, 3 (1), 111-116. https://doi.org/10.5281/zenodo.18169650
10.5281/zenodo.18169650
