Similarly to Algorithm 1, Algorithm 2 requires (i) the input text, (ii) the taxonomy T , (iii) the set of tuples C, (iv) the threshold θ f and (v) the level of generalization d. In lines 6 − 25, a term c i is candidate for generalization when its frequency f i is below the specified threshold θ f (line 7).
nevertheless, semantic-based methods are not able to achieve comparable performance to deep learning approaches (Gupta and Gupta, 2019) and for this reason, a framework utilizing semantic-based data generalization for the enhancement of sequenceto-sequence (seq2seq) deep learning abstractive summarization is presented in this work.