Extractive Text Summarization Using Recent Approaches: A Survey

ive Models for Long Text Summarization, 2017 [21] Machine Learning Approach, Evaluation metric used ROUGE (RN, R-L) Sentence extraction, Summary generation In this paper evaluated reference summary and system summary. SummaRuNNer: A Recurrent Neural Network Based Sequence Modelfor Extractive Summarization of Documents, 2017 [22] Machine Learning Approach Evaluation metric used ROUGE (R-N, R-L) Information Content, Salience, Novelty etc. Calculating results for SummaRuNNer on different dataset using ROUGE. Use of fuzzy logic and wordnet for improving performance of extractive automatic text summarization, 2016 [23] Linguistic approach Evaluation uses 95 documents and ROUGE metric on DUC 2002 datasets: R-1 and R-2 Title similarity, Sentence Centrality, Numerical Data, Sentence Scoring, Sentence length Performance evaluating for 95 documents through ROUGE-N (i.e., N= 1 and 2). Calculated precision, recall and f-measure. Extractive single-document summarization based on genetic operators and guided local search, 2014 [24] Machine Learning Evaluation Uses ROUGE metric: ROUGE-1, ROUGE-2 Title, Length, Position, Cohesion etc. Measuring performance of MASingleDocSum, DE and FEOM etc. through two Datasets DUC 2001 and 2002 etc. Combining Syntax and Semantics for Automatic Extractive SingleDocument Summarization, 2012 [25] Statistical Approach Evaluation uses ROUGE metric for n-grams cooccurrence. TextRank Score, WordNet Score, Position Score Performance on ROUGE n-grams for MEAD and TextRank Sentence extraction for article YB and NB execution. Single document extractive text summarization using Genetic Algorithms, 2012 [26] Linguistic Approach, Machine Learning Cohesion Factor (CF), Readability Factor (RF), Topic Relation Factor (TRF) Calculating the recall, precision value of summary through DUC 2002. SumCR: A new subtopic-based extractive approach for text summarization 2012 [27] Machine Learning Evaluation uses ROUGE metric (ROUGE-2 and ROUGE-SU4) Keywords, title, sentence location, and cue words Performance measure for the SumCR-Q, SumCR-G, System-24 etc. Automated extractive singledocument summarization: beating the baselines with a new approach, 2011 [28] Statistical Approach Evaluation uses ROUGE metric for compare SynSem. Total Sentence score, TextRank score, Position Score and WordNet Score Performance measure for SynSem and on the DUC 2002, first 100 words of each article. Integrating Prosodic Features in Extractive Meeting Summarization 2009, [29] Machine Learning (SemiSupervised Learning) Evaluation uses ROUGE metric. Local context information, topic and speakers. Performance measuring for prosodic and nonprosodic information. A Probabilistic Generative Framework for Extractive Broadcast News Speech Summarization, 2008 [30] Machine learning Evaluation uses ROUGE metric. Features are Lexical, Prosodic, Confidence and Relevance. Here performing result on Chinese broadcast news on the different models. Summarizing Scientific Texts: Experiments with Extractive Summarizers, 2007 [31] Linguistic Approach Evaluation uses ROUGE metric: (R-1) Sentence order, Sentence position Evaluating precision, recall and f-measure through ROUGE metrics. Event-based Extractive Summarization using Event Semantic Relevance from External Linguistic Resource 2007, [32] Linguistic Approach Evaluation uses ROUGE metric: (ROUGE-1, ROUGE-2 and ROUGEW) Semantic relation Performance measuring for semantic relation, normalization format of strength, bi-relation and parallel connection model. Sentence-extractive automatic speech summarization and evaluation techniques, 2006, [33] Linguistic Approach (Fmeasure, and 2 and 3gram recall) Sentence Extraction (Confidence score, Linguistic score) etc. Evaluating to the performance of the sentence f-measure and N-gram recall and finding