A Data-driven Latent Semantic Analysis for Automatic Text Summarization using LDA Topic Modelling

• URL: http://arxiv.org/abs/2207.14687v7
• Date: Tue, 30 May 2023 01:13:18 GMT
• Title: A Data-driven Latent Semantic Analysis for Automatic Text Summarization using LDA Topic Modelling
• Authors: Daniel F. O. Onah, Elaine L. L. Pang, Mahmoud El-Haj
• Abstract summary: This study presents the Latent Dirichlet Allocation (LDA) approach used to perform topic modelling. The visualisation provides an overarching view of the main topics while allowing and attributing deep meaning to the prevalence individual topic. The results suggest the terms ranked purely by considering their probability of the topic prevalence within the processed document.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the advent and popularity of big data mining and huge text analysis in modern times, automated text summarization became prominent for extracting and retrieving important information from documents. This research investigates aspects of automatic text summarization from the perspectives of single and multiple documents. Summarization is a task of condensing huge text articles into short, summarized versions. The text is reduced in size for summarization purpose but preserving key vital information and retaining the meaning of the original document. This study presents the Latent Dirichlet Allocation (LDA) approach used to perform topic modelling from summarised medical science journal articles with topics related to genes and diseases. In this study, PyLDAvis web-based interactive visualization tool was used to visualise the selected topics. The visualisation provides an overarching view of the main topics while allowing and attributing deep meaning to the prevalence individual topic. This study presents a novel approach to summarization of single and multiple documents. The results suggest the terms ranked purely by considering their probability of the topic prevalence within the processed document using extractive summarization technique. PyLDAvis visualization describes the flexibility of exploring the terms of the topics' association to the fitted LDA model. The topic modelling result shows prevalence within topics 1 and 2. This association reveals that there is similarity between the terms in topic 1 and 2 in this study. The efficacy of the LDA and the extractive summarization methods were measured using Latent Semantic Analysis (LSA) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics to evaluate the reliability and validity of the model.

Related papers

• Investigating the Impact of Text Summarization on Topic Modeling [13.581341206178525]
  In this paper, an approach is proposed that further enhances topic modeling performance by utilizing a pre-trained large language model (LLM) Few shot prompting is used to generate summaries of different lengths to compare their impact on topic modeling. The proposed method yields better topic diversity and comparable coherence values compared to previous models.
  arXiv  Detail & Related papers  (2024-09-28T19:45:45Z)
• Interactive Topic Models with Optimal Transport [75.26555710661908]
  We present EdTM, as an approach for label name supervised topic modeling. EdTM models topic modeling as an assignment problem while leveraging LM/LLM based document-topic affinities.
  arXiv  Detail & Related papers  (2024-06-28T13:57:27Z)
• FENICE: Factuality Evaluation of summarization based on Natural language Inference and Claim Extraction [85.26780391682894]
  We propose Factuality Evaluation of summarization based on Natural language Inference and Claim Extraction (FENICE) FENICE leverages an NLI-based alignment between information in the source document and a set of atomic facts, referred to as claims, extracted from the summary. Our metric sets a new state of the art on AGGREFACT, the de-facto benchmark for factuality evaluation.
  arXiv  Detail & Related papers  (2024-03-04T17:57:18Z)
• Exploring the Power of Topic Modeling Techniques in Analyzing Customer Reviews: A Comparative Analysis [0.0]
  Machine learning and natural language processing algorithms have been deployed to analyze the vast amount of textual data available online. In this study, we examine and compare five frequently used topic modeling methods specifically applied to customer reviews. Our findings reveal that BERTopic consistently yield more meaningful extracted topics and achieve favorable results.
  arXiv  Detail & Related papers  (2023-08-19T08:18:04Z)
• Multimodal Relation Extraction with Cross-Modal Retrieval and Synthesis [89.04041100520881]
  This research proposes to retrieve textual and visual evidence based on the object, sentence, and whole image. We develop a novel approach to synthesize the object-level, image-level, and sentence-level information for better reasoning between the same and different modalities.
  arXiv  Detail & Related papers  (2023-05-25T15:26:13Z)
• Topics in the Haystack: Extracting and Evaluating Topics beyond Coherence [0.0]
  We propose a method that incorporates a deeper understanding of both sentence and document themes. This allows our model to detect latent topics that may include uncommon words or neologisms. We present correlation coefficients with human identification of intruder words and achieve near-human level results at the word-intrusion task.
  arXiv  Detail & Related papers  (2023-03-30T12:24:25Z)
• Topic-Aware Encoding for Extractive Summarization [15.113768658584979]
  We propose a topic-aware encoding for document summarization to deal with this issue. A neural topic model is added in the neural-based sentence-level representation learning to adequately consider the central topic information. The experimental results on three public datasets show that our model outperforms the state-of-the-art models.
  arXiv  Detail & Related papers  (2021-12-17T15:26:37Z)
• ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining [61.82562838486632]
  We crowdsource four new datasets on diverse online conversation forms of news comments, discussion forums, community question answering forums, and email threads. We benchmark state-of-the-art models on our datasets and analyze characteristics associated with the data.
  arXiv  Detail & Related papers  (2021-06-01T22:17:13Z)
• Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding [71.2260967797055]
  We propose a weakly-supervised approach for aspect-based sentiment analysis. We learn sentiment, aspect> joint topic embeddings in the word embedding space. We then use neural models to generalize the word-level discriminative information.
  arXiv  Detail & Related papers  (2020-10-13T21:33:24Z)
• Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks [21.379555672973975]
  This paper proposes a graph neural network (GNN)-based extractive summarization model. Our model integrates a joint neural topic model (NTM) to discover latent topics, which can provide document-level features for sentence selection. The experimental results demonstrate that our model achieves substantially state-of-the-art results on CNN/DM and NYT datasets.
  arXiv  Detail & Related papers  (2020-10-13T09:30:04Z)
• From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information [77.89755281215079]
  Text summarization is the research area aiming at creating a short and condensed version of the original document. In real-world applications, most of the data is not in a plain text format. This paper focuses on the survey of these new summarization tasks and approaches in the real-world application.
  arXiv  Detail & Related papers  (2020-05-10T14:59:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.