Breaking Down Computer Networking Instructional Videos: Automatic Summarization with Video Attributes and Language Models

Video streaming over wireless networks becomes increasingly important in a variety of applications. To accommodate the dynamic change of wireless networks, quality of service (QoS) scalable video streams need to be provided. This paper presents a system of content-aware wireless streaming of lecture (instructional) videos for e-learning applications. A method for real-time analysis of instructional videos is first provided to detect video content regions and classify video frames, then a 'leaking video buffer model is applied to dynamically compress video streams. In our content-aware video streaming, instructional video content is detected and different QoS are selected for different types of video content. Our adaptive feedback control scheme is able to transmit properly compressed video streams to video clients not only based on wireless network bandwidth, but also based on video content and the feedback of video clients. Finally, we demonstrate the scalability and content awareness of our system and show experimental results of two lecture videos.

The exponential growth of textual data, automatic text summarization has become an essential tool for extracting key information. It presents an end-to-end implementation of an abstractive text summarization model based on transformer architectures. The proposed system leverages a pre-trained language model, such as BART or T5 to generate coherent and concise summaries. The deployment is automated using GitHub Actions, ensuring a seamless CI/CD pipeline for real-world applications. Experimental results demonstrate the effectiveness of the model in generating high-quality summaries with improved ROUGE scores compared to baseline methods. This implementation provides a robust and automated solution for text summarization, making it adaptable for various NLP applications in academia and industry. Hence, automatic text summarization has become a desirable tool in today’s information age. It produces concise, fluent and readable summaries from larger bodies of text. Keywords: NLP, Abstractive Summarization, Transformers, Docker, GitHub-Actions, Cloud Deployment

Increased availability of the Internet and social media has created another ‘world of data’ comprised of text, audio and video files. It is very difficult for a user to get the accurate summary or to comprehend the relevant and important items from the available media. Additionally, readers or evaluators of these data files are interested only in the relevant content or summary to be retrieved in the less duration from the source files. Automatic text summarization (ATS) is the only way to summarize single or multiple documents to obtain relevant content from the source files. Available ATS systems generate bad summaries and take a lot of time and space for long documents due to inaccurate encoding. Therefore, in this work, we have introduced an approach for extractive text summarization using sentence ranking. Experiments have been performed over BBC and CNN news datasets and evaluated in terms of ROUGE using N-gram Language Model. The quantitative values of the metrics show the effectiveness of the proposed approach for news datasets.

Quality of bowel preparation and patient knowledge remains a major barrier for completing colorectal cancer screening. Few studies have tested unique ways to impact patient understanding centering on interactive computer programs, pictures, and brochures. Two studies explored instructional videos but focused on patient compliance and anxiety as endpoints. Furthermore, excessive video length and content may limit their impact on a broad patient population. No study so far has studied a video's impact on preparation quality and patient understanding of the colonoscopy procedure. We conducted a single blinded prospective study of inner city patients presenting for a first time screening colonoscopy. During their initial visit patients were randomized to watch an instructional colonoscopy video or a video discussing gastroesophageal reflux disease (GERD). All patients watched a 6 minutes long video with the same spokesperson, completed a demographic questionnaire (Supplemental Digital Content 1, http://links.lww.com/JCG/A352) and were enrolled only if screened within 30 days of their visit. On the day of the colonoscopy, patients completed a 14 question quiz of their knowledge. Blinded endoscopist graded patient preparations based on the Ottawa scale. All authors had access to the study data and reviewed and approved the final manuscript. Among the 104 subjects enrolled in the study, 56 were in the colonoscopy video group, 48 were in GERD video group, and 12 were excluded. Overall, 48% were male and 52% female; 90% of patients had less than a high school education, 76% were African American, and 67% used a 4 L split-dose preparation. There were no differences between either video group with regard to any of the above categories. Comparisons between the 2 groups revealed that the colonoscopy video group had significantly better Ottawa bowel preparation score (4.77 vs. 6.85; P=0.01) than the GERD video group. The colonoscopy video group also had less-inadequate repeat bowel preparations versus the GERD video group (9% vs. 23%; P<0.01). The overall score on the knowledge questionnaire (Supplemental Digital Content 1, http://links.lww.com/JCG/A352) was significantly higher in the colonoscopy video group as compared with the GERD video group (12.77 vs. 11.08; P<0.001. In all patients the overall quiz score positively correlated with preparation quality (odds ratio, 2.31; confidence interval, 1.35-3.94; P<0.001). Our unique population represented an overwhelmingly under-educated (85% had a high school education or less) and minority group (76% African American). They are one of the most at risk of having multiple barriers such as comprehension and reading difficulties resulting in poor preparation examinations and no shows to procedures. Our instructional video proved to be high yield in this population. The patients assigned to watch the colonoscopy video showed a significant increase in "excellent" grade adequate bowel preparation quality by >23% and a significant decrease in "inadequate" bowel preparations by almost 50%. Our study proves that an educational video can improve both comprehension with regard to all aspects of colonoscopy. ClinicalTrials.gov number, NCT02906969.

With the overflowing of textual data cross digital platforms, automatic text summarization has become increasingly essential for efficient information retrieval, knowledge distillation, and content comprehension. Abstractive summarization, in particular, seeks to produce concise and coherent summaries by paraphrasing and reinterpreting the original content rather than simply extracting key sentences.In this work, we propose an abstractive summarization approach based on large language models (LLMs), combining the strengths of Mistral 7B and BART. The proposed hybrid method aims to improve summary quality by generating outputs from both models, segmenting the summaries into individual sentences, and comparing them using cosine similarity to identify semantically aligned content. Subsequently, BERTScore is applied to select the most relevant sentence for inclusion in the final summary.To further enhance performance, the Mistral 7B model was fine-tuned on the CNN/DailyMail dataset using optimization techniques such as QLoRA, Gradient Checkpointing, LoRA, and GPTQ. Experimental results demonstrate that our approach produces more accurate and informative summaries, outperforming the individual models in both ROUGE and BERTScore metrics. These findings underline the potential of hybrid LLM fusion for generating higher-quality abstractive summaries.

Automatic summarization of legal public opinion texts poses unique challenges due to complex temporal dynamics and nuanced causal relationships inherent in legal events discussed in news and social media. Existing summarization approaches, including powerful large language models (LLMs), often fail to capture these critical temporal sequences and causal links, resulting in summaries that lack coherence and narrative logic. To address this, we propose a novel summarization framework that explicitly integrates temporal and causal relationship modeling into an LLM-based architecture, specifically finetuning the Qwen language model with extracted event timelines and causal connections. Evaluations conducted on the publicly available CAIL-2022 legal public opinion dataset, demonstrate that our proposed method significantly outperforms traditional summarizers and baseline LLMs across metrics such as ROUGE1, ROUGE-2, ROUGE-L and BERTScore. Experimental analysis further confirms that the generated summaries exhibit enhanced chronological coherence and accurately reflect causal event structures. This study provides compelling evidence that embedding temporal and causal reasoning into LLM-based summarization substantially improves the quality and informativeness of summaries in the legal public opinion domain, offering promising directions for future research and practical applications in public opinion analysis.

The amount of digital video content available to users is rapidly increasing. Developments in computer, digital network, and storage technologies all contribute to broaden the offer of digital video. Only users’ attention and time remain scarce resources. Users face the problem of choosing the right content to watch among hundreds of potentially interesting offers. Video and audio have a dynamic nature: they cannot be properly perceived without considering their temporal dimension. This property makes it difficult to get a good idea of what a video item is about without watching it. Video previews aim at solving this issue by providing compact representations of video items that can help users making choices in massive content collections. This thesis is concerned with solving the problem of automatic creation of video previews. To allow fast and convenient content selection, a video preview should take into consideration more than thirty requirements that we have collected by analyzing related literature on video summarization and film production. The list has been completed with additional requirements elicited by interviewing end-users, experts and practitioners in the field of video editing and multimedia. This list represents our collection of user needs with respect to video previews. The requirements, presented from the point of view of the end-users, can be divided into seven categories: duration, continuity, priority, uniqueness, exclusion, structural, and temporal order. Duration requirements deal with the durations of the preview and its subparts. Continuity requirements request video previews to be as continuous as possible. Priority requirements indicate which content should be included in the preview to convey as much information as possible in the shortest time. Uniqueness requirements aim at maximizing the efficiency of the preview by minimizing redundancy. Exclusion requirements indicate which content should not be included in the preview. Structural requirements are concerned with the structural properties of video, while temporal order requirements set the order of the sequences included in the preview. Based on these requirements, we have introduced a formal model of video summarization specialized for the generation of video previews. The basic idea is to translate the requirements into score functions. Each score function is defined to have a non-positive value if a requirement is not met, and to increase depending on the degree of fulfillment of the requirement. A global objective function is then defined that combines all the score functions and the problem of generating a preview is translated into the problem of finding the parts of the initial content that maximize the objective function. Our solution approach is based on two main steps: preparation and selection. In the preparation step, the raw audiovisual data is analyzed and segmented into basic elements that are suitable for being included in a preview. The segmentation of the raw data is based on a shot-cut detection algorithm. In the selection step various content analysis algorithms are used to perform scene segmentation, advertisements detection and to extract numerical descriptors of the content that, introduced in the objective function, allow to estimate the quality of a video preview. The core part of the selection step is the optimization step that consists in searching the set of segments that maximizes the objective function in the space of all possible previews. Instead of solving the optimization problem exactly, an approximate solution is found by means of a local search algorithm using simulated annealing. We have performed a numerical evaluation of the quality of the solutions generated by our algorithm with respect to previews generated randomly or by selecting segments uniformly in time. The results on thirty content items have shown that the local search approach outperforms the other methods. However, based on this evaluation, we cannot conclude that the degree of fulfillment of the requirements achieved by our method satisfies the end-user needs completely. To validate our approach and assess end-user satisfaction, we conducted a user evaluation study in which we compared six aspects of previews generated using our algorithm to human-made previews and to previews generated by subsampling. The results have shown that previews generated using our optimization-based approach are not as good as manually made previews, but have higher quality than previews created using subsample. The differences between the previews are statistically significant.

One of the most important tasks in NLP (Natural Language Processing) is automatic text summarization which aims to distill vast amounts of textual information into a brief summary while maintaining its essential meaning. Notwithstanding notable progress earlier research in this field has been constrained by the incapacity to accurately summarize various datasets and capture contextual subtleties. Scalability generalization and striking a balance between summary coherence and informativeness are common issues with current models. Using the Conditional Generative Adversarial Network (CGAN) technique we present a novel framework for automatic text summarization that incorporates data augmentation in order to overcome these constraints. In order to ensure that the text is clean and prepared for additional analysis the suggested framework starts with a thorough pre-processing phase that includes tokenization hashtag and username elimination stop word removal punctuation removal stemming and lemmatization. The Robustly Optimised BERT Approach (RoBERTa) which we use for word embedding greatly improves contextual representation and text comprehension. A hybrid approach that combines ShuffleNet V2 and SE-ResNeXt-101 is used for feature extraction ensuring effective extraction of discriminative features. Ultimately the Modified Electric Eel Foraging Optimisation (MEEFO) algorithm is used to optimise the summary generation. This algorithm explores the trade-off between precision and recall in order to fine-tune the summary quality. Experimental results show that the suggested method is more effective than current approaches with notable gains in ROUGE-L scores and overall summary quality across a number of benchmark datasets. During training and testing the model achieves higher accuracy and lower loss demonstrating its generalizability and resilience.

Instructional videos provide detailed how-to guides for various tasks, with viewers often posing questions regarding the content. Addressing these questions is vital for comprehending the content, yet receiving immediate answers is difficult. While numerous computational models have been developed for Video Question Answering (Video QA) tasks, they are primarily trained on questions generated based on video content, aiming to produce answers from within the content. However, in real-world situations, users may pose questions that go beyond the video's informational boundaries, highlighting the necessity to determine if a video can provide the answer. Discerning whether a question can be answered by video content is challenging due to the multi-modal nature of videos, where visual and verbal information are intertwined. To bridge this gap, we present the YTCommentQA dataset, which contains naturally-generated questions from YouTube, categorized by their answerability and required modality to answer -- visual, script, or both. Experiments with answerability classification tasks demonstrate the complexity of YTCommentQA and emphasize the need to comprehend the combined role of visual and script information in video reasoning. The dataset is available at https://github.com/lgresearch/YTCommentQA.

Language models are generally employed to estimate the probability distribution of various linguistic units, making them one of the fundamental parts of natural language processing. Applications of language models include a wide spectrum of tasks such as text summarization, translation and classification. For a low resource language like Bengali, the research in this area so far can be considered to be narrow at the very least, with some traditional count based models being proposed. This paper attempts to address the issue and proposes a continuous-space neural language model, or more specifically an ASGD weight dropped LSTM language model, along with techniques to efficiently train it for Bengali Language. The performance analysis with some currently existing count based models illustrated in this paper also shows that the proposed architecture outperforms its counterparts by achieving an inference perplexity as low as 51.2 on the held out data set for Bengali.

With the popularity of large language models and their high-quality text generation capabilities, researchers are using them as auxiliary tools for text summary writing.Although summaries generated by these large language models are smooth and capture key information sufficiently, the quality of their output depends on the prompt, and the generated text is somewhat procedural to a certain extent.We construct LecSumm to verify whether language models truly capture human writing preferences, in which we recruit 200 college students to write summaries for lecture notes on ten different machine-learning topics and analyze writing preferences in real-world human summaries through the dimensions of length, content depth, tone & style, and summary format.We define the method of capturing human writing preferences by language models as finetuning pre-trained models with data and designing prompts to optimize the output of large language models.The results of translating the analyzed human writing preferences into prompts and conducting experiments show that both models still fail to capture human writing preferences effectively.Our LecSumm 1 dataset brings new challenges to finetuned and prompt-based large language models on the task of human-centered text summarization.

Language model is widely used in many retrieval systems. Its document representation is based on the bag of words assumption. Hence, each term in document is treated as an equal object and only the term frequency is considered as the evidence of the importance of term. In this paper, we study the problem of cognition attention attenuation in processing documents and present a cognition attention attenuation based language model. This model estimates the document model by attenuation process of term in document. Compared with the classical language model, the advantage of this model is considering about the document structure which is often used in text summarization. From the experiments results, our novel cognition attention attenuation based language model outperformed the classical language model with Dirichlet smoothing in blog page and Web page.

Text summarization plays a pivotal role in condensing crucial information from huge volumes of text. This study investigates the utilization of pre-trained transformer models within the domain of text summarization, with a specific emphasis on extreme summarization. It delves into the effectiveness of two prominent models, text-to-text transfer transformers and bidirectional and auto-regressive transformers, when applied to the task of summarization, providing a comparative analysis of their capabilities. The experiments conducted in this study involve the utilization of the XSum and SciTLDR datasets. While fine-tuning T5 and BART on summarization tasks is a standard approach, we delve into the performance of these models without fine-tuning. Additionally, we explore the potential of other pretrained models, such as PREGASUS and distilBART, in generating concise and coherent summaries. This study contributes to the understanding of how pre-trained transformer models can be harnessed effectively for text summarization, especially in extreme summarization scenarios. The findings shed light on the performance, challenges, and potential of these models, opening avenues for further research in the field of automatic text summarization.

As video summarization techniques have attracted more and more attention for efficient multimedia data management, quality assessment of video summary is required. To address the lack of automatic evaluation techniques, this paper proposes a novel framework including several new algorithms to assess the quality of the video summary against a given reference. First, we partition the reference video summary and the candidate video summary into the sequences of summary unit (SU). Then, we utilize alignment based algorithm to match the SUs in the candidate summary with the SUs in the corresponding reference summary. Third, we propose a novel similarity based 4 C - assessment algorithm to evaluate the candidate video summary from the perspective of coverage, conciseness, coherence, and context, respectively. Finally, the individual assessment results are integrated according to userpsilas requirement by a learning based weight adaptation method. The proposed framework and techniques are experimented on a standard dataset of TRECVID 2007 and show the good performance in automatic video summary assessment.

The "AI YouTube Video Summary using NLP" project introduces an innovative solution to the burgeoning challenge of digesting vast amounts of video content on platforms like YouTube. With the exponential growth of online video, users often face time constraints and information overload, hindering their ability to extract valuable insights efficiently. Our project addresses this issue by harnessing the capabilities of Artificial Intelligence (AI) and Natural Language Processing (NLP) to automatically generate concise summaries of YouTube videos. Through a seamless integration with the MERN stack, our system enables users to input video URLs and receive summaries in three distinct forms: short, long, and key insights. By automating the process of transcript extraction, linguistic analysis, and summarization, our system streamlines content consumption, offering users a time-saving and effective method for accessing essential information. By leveraging machine learning algorithms and linguistic analysis techniques, our system accurately identifies and distills key themes, concepts, and insights embedded within the video content. This empowers users to gain comprehensive understanding without the need for exhaustive viewing, thereby enhancing their browsing experience and knowledge acquisition. In essence, the "AI YouTube Video Summary using NLP" project represents a significant advancement in content consumption methodologies, offering a practical solution to the challenges posed by the proliferation of video content online. Through our innovative approach, we aim to revolutionize the way users engage with YouTube videos, facilitating efficient information extraction and empowering them to make the most of their online viewing experience. Keywords: Artificial Intelligence (AI), Natural Language Processing (NLP), Text Summarization, Multimedia Content Analysis, Automatic Summarization.