Knowledge Graph and Deep Neural Network for Extractive Text Summarization by Utilizing Triples (2020)

Abstract: In our research work, we represent the content of the sentence in graphical form after extracting triples from the sentences. In this paper, we will discuss novel methods to generate an extractive summary by scoring the triples. Our work has also touched upon sequence-to-sequence encoding of the content of the sentence, to classify it as a summary or a non-summary sentence. Our findings help to decide the nature of the sentences forming the summary and the length of the system generated summary as compared to the length of the reference summary.

## introduction extractive summaries contain the most informative sentences from the input text. the ordered pair of subject(s), verb(v) and object(o) i.e. 1 < s, v, @xmath0. we form a knowledge-graph(kg) by considering words in the triples. our novel methods choose the informative sentences based on the count of frequencies calculated using generated kg. we also have implemented machine learning(ml) and deep neural network(dnn) based models. these models make use of the kg based features which try to capture information available. we are making use of dataset made available for fns-2020 shared task by @xcite . 2 we have used spacy library for extracting triples. we have used python implementation of rouge package made available by 3 pypi, which implements rouge described by @xcite . ## implemented approaches in general, we pose extractive summarization as a sentence classification and a triple classification task. we perform this classification using algorithms like svm, svr, neural network(nn) and long short-term memory(lstm/dnn). this section describes our implemented approaches in details. we have considered features as, position of the sentenceeach sentence is marked according to its position (i.e. 1-introductory, 2-concluding and 3-explanatory), lengthnumber of words present in the sentence, thematic wordsnumber of most foot_0 frequent words present in the sentence, indicator words-count of words present in the available foot_1 synset(i.e. the group of synonymous words) of words 'conclusion' and 'summary', uppercasenumber of uppercase words present in the foot_2 sentence, important word feature-it represents quotient of the available triple in the sentence to the total triples in the text file, and kg file feature-it represents quotient of the available triples in the sentence represented in terms of foot_3 lookup frequencies to the number of total triples in the text file. we also have used pre-trained 100-dimensional glove @xcite embedding for dnn based approaches. ## results and analysis we have implemented eight different approaches while considering textrank @xcite as the baseline approach. in this section, we discuss the performance of the implemented models on the validation set followed by a performance on the test set.