Anaphora solvED Ad-DL-BERT model for text summarization with auto encoding using the topic description and several priors (ATDS) approach

Abstract

Although several models for automatic text summarization exist, there are still limitations – like the anaphora problem that occurs during summarizations. To overcome such limitations, this paper proposes the Added dropout- Deleted Layer norm-Bidirectional Encoder Representations from Transformers (Ad-DL-BERT)-based extractive text summarization (ETS). Primarily, the input document’s sentences are prepared for accurate summarization by preprocessing; then, the unwanted sentences are removed. With the Auto encoding using the Topic Description and Several priors (ATDS) approach, any sentences under the same topic are clustered afterwards. Moreover, keywords for summarization are extracted with an AnaphoraPOS (An-POS) extractor. For removing the redundant sentences, the rankings with Exponential Linear Unit- Generative Adversarial Network (ELU-GAN) and saliency score assignment processes are performed thereafter. Also, assignments for sentences are performed to enhance the coherency, sorting, and cosine-similarity score. Lastly, the Ad-DL-BERT-generated summary and the proposed technique’s performance are evaluated on the document understanding conference (DUC2002) data set. Regarding the clustering time, execution time, recall-oriented understudy for the gisting evaluation (ROUGE-1) scores of recall, F-measure, and precision, the experimental outcomes exhibited the proposed technique’s dominance over the conventional approaches.