Towards Automatic Textual Summarization of Movies

Liu, Chang; Shmilovici, Armin

doi:10.1007/978-3-030-47124-8_39

Cited by 3 publications

(7 citation statements)

References 24 publications

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“…2 summarizes the extractive text summarization process. This section discusses the previous work done to summarize educational videos, which can be divided into three categories (i) video summarization based on audio, visual scenes, and subtitles [3,5,7,8,[16][17][18], (ii) audio-only outlines [6], and (iii) subtitles-only summarization [3,[5][6][7][8][9][10][11][12][13][14]. Speech recognition faces some challenges when generating text summaries [6], e.g., lack of punctuations [9].…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, multimedia news was summarized using Multimodal LDA (MMLDA) [5]. Furthermore, video summarization has been applied to movies [8]. Researchers in [8] summarized 156 scenes of the Forrest Gump movie based on scenes' description and subtitles analysis.…”

Section: Summarization Based On Audio Visual and Subtitlesmentioning

confidence: 99%

“…Summarizing educational videos benefits learners by saving their time and storage space and making the searching and indexing process quick and easy [3][4][5]. A video can be summarized based on its audio [3,[5][6][7], visual [3,5,7,8], and textual [3,[5][6][7][8][9][10][11][12][13][14] data, i.e., subtitles. The video's audio is summarized by converting the spoken words to text using speech-to-text techniques and applying text-based summarization methods [6].…”

Section: Introductionmentioning

confidence: 99%

“…Whereas in subtitles summarization, the textual data is summarized using textbased summarization algorithms [9]. On the other hand, the generated summaries could be either a short video [17] or a text [3][4][5][6][7][8][9][10][11][12][13][14]16,18].…”

Section: Introductionmentioning

confidence: 99%

“…The abstractive summarization summarizes a document like human summarization using external vocabulary and paraphrasing [4]. In contrast, extractive summarization summarizes a document by extracting the exact sentences that are considered significant by calculating each sentence's frequency and importance score [3,[5][6][7][8][9][10][11][12][13][14]. Tab.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Educational Videos Subtitles’ Summarization Using Latent Dirichlet Allocation and Length Enhancement

Alrumiah¹,

Al-Shargabi²

2022

Computers, Materials &Amp; Continua

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Nowadays, people use online resources such as educational videos and courses. However, such videos and courses are mostly long and thus, summarizing them will be valuable. The video contents (visual, audio, and subtitles) could be analyzed to generate textual summaries, i.e., notes. Videos' subtitles contain significant information. Therefore, summarizing subtitles is effective to concentrate on the necessary details. Most of the existing studies used Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Semantic Analysis (LSA) models to create lectures' summaries. This study takes another approach and applies Latent Dirichlet Allocation (LDA), which proved its effectiveness in document summarization. Specifically, the proposed LDA summarization model follows three phases. The first phase aims to prepare the subtitle file for modelling by performing some preprocessing steps, such as removing stop words. In the second phase, the LDA model is trained on subtitles to generate the keywords list used to extract important sentences. Whereas in the third phase, a summary is generated based on the keywords list. The generated summaries by LDA were lengthy; thus, a length enhancement method has been proposed. For the evaluation, the authors developed manual summaries of the existing "EDUVSUM" educational videos dataset. The authors compared the generated summaries with the manual-generated outlines using two methods, (i) Recall-Oriented Understudy for Gisting Evaluation (ROUGE) and (ii) human evaluation. The performance of LDA-based generated summaries outperforms the summaries generated by TF-IDF and LSA. Besides reducing the summaries' length, the proposed length enhancement method did improve the summaries' precision rates. Other domains, such as news videos, can apply the proposed method for video summarization.

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