A topic-based sentence representation for extractive text summarization

Abstract

We examine the effect of probabilistic topic model-based word representations, on sentence-based extractive summarization. We formulate the task of sentence selection as a binary classification problem, and we test a variety of machine learning algorithms, exploring a range of different settings for classification and modelling. A preliminary investigation via a wide experimental evaluation on the MultiLing 2015 MSS dataset illustrates that topicbased representations can prove beneficial to the extractive summarization process, compared to a TF-IDF baseline, with Quadratic Discriminant Analysis and Gradient Boosting providing the best results for micro and macro F1 score, respectively.

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

“…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].…”

“…Additionally, others combined the power of topic modelling with the simplicity of extractive summarization to produce document summaries [14]. LDA proved its effectiveness in generating summaries as it improved the TF-IDF results.…”

“…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].…”

“…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].…”

“…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.…”