A Novel Framework for Semantic OrientedAbstractive Text Summarization

Abstract

Internet continues to be the most communal of all the mass media turning information content from being scarce to superabundant that is evidenced by its increase tenfold every five years. A powerful text summarizer can aid in balancing this overload but generating quality summaries of the target content thereby reducing time and effort to mine the required information. The proposed system aims is to develop a Semantic Oriented Abstractive Summarization to generate abstractive summaries with increased readability and qualitative content. The contribution of our works are Joint Model Predicate Sense Disambiguation and Semantic Role Labelling termed as Joint (PSD+SRL) is proposed to better capture the semantic representation of text. The content selection involves semantic based content selection and feature extraction are selected by Genetic Algorithm. The proposed system can be very useful for the students who want to read a whole book in a short time. Our experimental study is carried out using DUC, a typical corpus for text summarization.

HSFO: Hunter Sail Fish Optimizer enabled deep learning for single document abstractive summarization based on semantic role labelling for Telugu text

“…Moratanch, N. and Chitrakala, S., [1] developed Joint Model of Predicate Sense Disambiguation and SR L (PSD + SRL) to capture semantic representation of text. This method worked well for creating an abstract summary with excellent clarity.…”

“…• PSD+SRL in [1] was introduced for semantic oriented abstractive text summary, and it proved to be highly helpful for students who wanted to finish a book q uickly. The technique does not, however, take into account using a voice recognition system to condense lengthy speeches.…”

“…HSFO_LSTM-CNN is compared with many methods like Joint Model of PSD + SRL [1], SRL-ESA [3], RNN [4], and Co-opNet [5]. The comparison is done by varying metrics, such as precision, rouge, recall, as well as F-measure.…”

“…For NLP tasks, such as Question Answering, Text Classification, and other associated areas of computer science like Information Retrieval, Automatic Text Summarization (ATS) [22] is very helpful. Development of automated systems that is particularly concerned with information retrieval from vast amount of unstructured documents on Web, for which text summarization systems are useful, has received lot of attention [1]. Difficulty of input text has made ATS one of the most difficult jobs in NLP.…”