# A Novel Approach for Semantic Extractive Text Summarization
## 2 Background  
The text summarization systems started in the early 1950s by [PERSON] and [PERSON] on the surface-level approach where the word frequency and word position attributes are used in the single document text summarization of technical articles [8; 9]. [PERSON]'s algorithm works on a bag-of-words model. It counts the number of words in the document and compresses the text documents based on thematic words. The thematic words of the document show how the word is important in the document. Its importance can be calculated based on the frequency of the words or how many times the word is repeated in the document. In the 1960s, the other attributes, such as cue words and cue phrases, combined with the previous ones that were used by [PERSON] on the surface-level approach for single document text summarization [10]. In 1995, trainable document summarizers were developed using machine learning techniques [11]. In this type of technique, multiple samples of summaries are given as an input to the summarizer system; it finds the relation between the sentences and their title or document and decides whether it includes summary sentences or not. In 1997, [PERSON] and [PERSON] proposed a lexical chains model that provides the semantic structure of sentences. Lexical chains were constructed using some knowledge base that contains nouns and their various associations [12]. NER and information extraction techniques were applied to news articles, in which the number of words that co-occurred in the questions was extracted [13]. Cut and paste systems were developed by using the statistical technique; they maintain the cohesive structure of the text and create a text that flows logically in structure and meaning from beginning to end [7]. [PERSON] ([PERSON]) developed the domain-specific statistical summarizer, which summarizes news articles [14]. [PERSON] and [PERSON] developed the single-level lexical summarizer, which summarizes articles and generates lexically related sentences [2]. Graph-based summarizers were also developed [15]. LSA-based summarizers were developed, which extract semantically related sentences [16]. [PERSON] also worked on text summarization using the same approach (LSA) to produce a coherent summary [17]. Researchers developed models for single-document summarization based on joint extraction and syntactic compression. Our model chooses sentences from the document, identifies possible compressions based on constituency parses and scores those compressions with a neural model to produce the final summary [18]. [PERSON] also developed a neural model with a modification of fuzzy logic in order to produce a coherent summary [19]. [PERSON] developed summarizers based on TextRank, the text rank algorithm, which is the implementation of the page rank algorithm that is used in the Google search engine for ranking web pages [20]. An English news summarizer for the English learning of Indonesian students was developed based on tf-idf features. The topmost sentences are selected based on similarity keywords, for the limitation length of the document, only relevant, top most sentences are extracted. For the summary evaluation, the precision, recall, and f-measure score are used for the summary strength [21]. [PERSON] developed automatic text summarization based on thickening sentence scores; it fetches sentences with a higher value and density and lower length. The target increases sentence value and reduces unnecessary words and long sentences, which gives the top sentences list more value. Subsequently, that sentence score is equal to the total sentence terms' occurrence divided by the sentence words [22]. A domain-independent, statistical-based method was developed for single-document extractive summarization. The bigrams technique was used, which repeats more than once in the text and are good terms to describe the text's contents, called maximal frequent sentences. We also show that the frequency of repeating bigram terms that occur gives a good result [23].