Lecture2Note: Automatic Generation of Lecture Notes from Slide-Based Educational Videos

Xu, Chengpei; Wang, Ruomei; Lin, Sheng; Luo, Xiangfeng; Zhao, Baoquan; Shao, Lijie; Hu, Mengqiu

doi:10.1109/icme.2019.00159

Cited by 14 publications

(4 citation statements)

References 9 publications

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“…Regarding tasks and technologies, we identified some low-level tasks performed as basic steps, of which, many rely on deep learning approaches. For example, for textual video elements, several studies employed optical character recognition [14,21,29,32,49,50,54,64,67,102,109,114,123,138,152,160,161,179,195,255,261,262,265,273,276], keyword extraction [14,40,43,61,92,105,106,109,112,121,128,131,161,206,255,264,271], generic natural language processing methods (e.g., [29,127,128,194,243]), or utilized word embeddings (e.g., [54,…”

Section: Audio Featuresmentioning

confidence: 99%

“…Text-based summarization: This kind of summarization was achieved by sentence segmentation, named entity recognition, extraction of key phrase extract, subtitles, and other metadata using transcripts as a main source [14,117,245] and on-screen text in a few cases [29,261]. Methods that supported this task were term frequency-inverse document frequency [245], Naive Bayes [14], and Latent Semantic Analysis [267] for identifying the relevant information.…”

Section: Summarization Toolsmentioning

confidence: 99%

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A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness

Navarrete¹,

Nehring²,

Schanze³

et al. 2023

Preprint

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People increasingly use videos on the Web as a source for learning, be it for daily tasks or in formal as well as informal educational settings. To support this way of learning, researchers and developers are continuously developing tools, proposing guidelines, analyzing data, and conducting experiments. However, it is still not clear what characteristics a video should have to be an effective learning medium. In this paper, we present a comprehensive review of 257 articles on video-based learning from computer science databases for the period from 2016 to 2021 using the PRISMA guidelines. One of the aims of the review is to identify the video characteristics that have been explored by previous work. Based on our analysis, we suggest a taxonomy which organizes the video characteristics and contextual aspects into eight categories: (1) audio features, (2) visual features, (3) textual features, (4) instructor behavior, (5) learners' activities, (6) interactive features (quizzes, etc.), (7) production style, and (8) instructional design. Also, we identify four representative research directions: (1) proposals of tools to support video-based learning, (2) studies with controlled experiments, (3) data analysis studies, and (4) proposals of design guidelines for learning videos. We find that the most explored characteristics are textual features followed by visual features, learner's activities, and interactive features. Text of transcripts, video frames, and images (figures and illustrations) are most frequently used by tools that support learning through videos. The learner's activity is heavily explored through log files in data analysis studies, and interactive features have been frequently scrutinized in controlled experiments. We complement our review by contrasting research findings that investigate the impact of video characteristics on the learning effectiveness, report on tasks and technologies used to develop tools that support learning, and summarize trends in the development of design guidelines to produce learning videos.

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Section: Audio Featuresmentioning

confidence: 99%

Section: Summarization Toolsmentioning

confidence: 99%

A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness

Navarrete¹,

Nehring²,

Schanze³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Lecture videos are analyzed for the development of various applications that utilize the content of lecture videos, such as indexing [1][2][3][4][5], summarization [6][7][8][9][10][11], content extraction [12][13][14][15][16][17], search [18][19][20][21], and navigation [22][23][24][25]. Lecture videos captured in classrooms and conference rooms has digital slides projected on to the screen on stage, a common setup in modern classrooms and conference rooms.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Projection Screen Detection from Presentation Videos using CustomYOLOv7 Object Detection Method

Purushotham,

Ramani,

Bindu

2024

Preprint

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Lecture videos are analyzed for the development of various applications that involves indexing, summarization, content extraction, search, and navigation. Lecture videos captured in classrooms and conference rooms has digital slides projected on to the screen on stage. Projection screen detection is a crucial task for the extraction of slide region in such presentation videos. In this paper, we present an interesting approach for detecting the location of slide region in video frames using the You Only Look Once (YOLO) object detection model. First we train the custom YOLOv7 model on a labelled dataset of frames from presentation videos showing projected slides and then the trained model is applied on new images that are not used in training to predict the location of projector screens. We collected and annotated over 2000 frames from various presentation videos and then various augmented techniques are applied to prepare a dataset of 5000 images. We evaluated this method on our custom dataset and the results are compared with other popular object detection methods. Our experiments demonstrated that our custom YOLOv7 model outperforms basic YOLOv7 and Retinanet with regards to accuracy and computational effectiveness. Our results suggest that custom YOLOv7 provides a promising solution for projector screen detection and has the potential to be applied in various practical applications

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“…Especially given the current COVID-19 situation, online video lectures provide a much safer alternative to face-to-face lectures. Subsequently, methods for enhancing students' experience of watching lecture videos via artificial intelligence technologies have been proposed, e.g., repacking the text, graphics, charts and instructor's speech voice in the lecture video into an interactive notebook structure via multimedia analysis to enhance students' learning efficiency [29], and automatically retrieving lecture videos to assist students in understanding knowledge points via real-time speech recognition [14]. However, existing methods have been designed to improve students' experience, whereas little work has been done to improve instructors' experience of recording and updating lecture videos.…”

Section: Introductionmentioning

confidence: 99%

Autolv: Automatic Lecture Video Generator

Wang

Song

Jha

2022

2022 IEEE International Conference on Image Processing (ICIP)

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We propose an end-to-end lecture video generation system that can generate realistic and complete lecture videos directly from annotated slides, instructor's reference voice and instructor's reference portrait video. Our system is primarily composed of a speech synthesis module with few-shot speaker adaptation and an adversarial learning-based talkinghead generation module. It is capable of not only reducing instructors' workload but also changing the language and accent which can help the students follow the lecture more easily and enable a wider dissemination of lecture contents. Our experimental results show that the proposed model outperforms other current approaches in terms of authenticity, naturalness and accuracy. Here is a video demonstration of how our system works, and the outcomes of the evaluation and comparison: https://youtu.be/cY6TYkI0cog.

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Lecture2Note: Automatic Generation of Lecture Notes from Slide-Based Educational Videos

Cited by 14 publications

References 9 publications

A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness

A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness

An Efficient Projection Screen Detection from Presentation Videos using CustomYOLOv7 Object Detection Method

Autolv: Automatic Lecture Video Generator

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