Implementation and Evaluation of Evolutionary Connectionist Approaches to Automated Text Summarization

Abstract

Problem statement:Text summarization takes care of choosing the most significant portions of text and generates coherent summaries that express the main intent of the given document. This study aims to compare the performances of the three text summarization systems developed by the authors with some of the existing Summarization systems available. These three approaches to text summarization are based on semantic nets, fuzzy logic and evolutionary programming respectively. All the three represent approaches to achieve connectionism. Approach: First approach performs Part of Speech (POS) tagging, semantic and pragmatic analysis and cohesion. The second system under discussion was a new extraction based automated system for text summarization using a decision module that employs fuzzy concepts. Third system under consideration was based on a combination of evolutionary, fuzzy and connectionist techniques. Results: Semantic net approach performs better than the MS Word summarizer as far as the semantics of the original text was concerned. To compare our summaries with those of the well known MS Word, Intellexer and Copernic summarizers, we use DUC's human generated summaries as the bench-mark. The results were very encouraging. The second approach based on fuzzy logic results in an efficient system since fuzzy logic mimics decision making of humans. Third system showed promising results as far as precision and F-measure are concerned than all the other approaches. Conclusion: Our first approach used WordNet, a lexical database for English. Unlike other dictionaries, WordNet does not include information about etymology, pronunciation and the forms of irregular verbs and contains only limited information about usage. To overcome this limitation, we developed a new text summarizer based on fuzzy logic. As Text summarization application requires learning ability based on activation, we utilize ANN attribute through a connectionist model to achieve the best results.

Improving Salience-Based Multi-Document Summarization Performance using a Hybrid Sentence Similarity Measure

“…Scoring techniques are categorized into word scoring, sentence scoring, and graph scoring [11]. In word scoring techniques, sentences are assigned scores based on the significance of words and their frequency in the text [6] , [12], [13]. Notably, words like proper nouns, places, and objects, considered determinants, receive higher scores [14,15].…”

“…Text scoring methods consider formal properties such as bold, italicized, and underlined words [16]. Sentences beginning with phrases like 'Briefly,' 'Finally,' and 'As a result' are identified as sign phrases and subsequent sentences are deemed important [12]. Similarly, evaluation involves the text title, with sentences containing title words considered for inclusion in the summary, increasing their importance [17].…”

“…Sentence scoring methods also consider sentence length, giving more weight to longer sentences [16], [18]. Scoring involves assigning points based on sentence position and whether it includes numerical values [12], [14], [19].…”

Extractive Text Summarization Using LSTM-Based Encoder-Decoder Classification

“…There are various methods proposed which are based on sentence scoring. 'Word Frequency' factor is considered in (21,22,23,24,25). If the number of times a word occurs in a document is more then, the word score is more.…”