A new sentence similarity measure and sentence based extractive technique for automatic text summarization

By Ramiz M. Aliguliyev. Expert systems with applications, 2009

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TakeLab: Systems for Measuring Semantic Text Similarity (2012, background): “Natural language processing tasks such as text clas-siﬁcation (Sebastiani, 2002), text summarization (Lin and Hovy, 2003; Aliguliyev, 2009), information retrieval (Park et al., 2005), and word sense disambiguation (Sch¨utze, 1998) rely on a measure of semantic similarity of textual documents.”
Multiple documents summarization based on evolutionary optimization algorithm (2013, methodology): “…clustering-based approaches control redundancy in the final summary by clustering sentences to identify themes of common information and selecting one or two representative sentences from each cluster into the final summary (Alguliev & Aliguliyev, 2008; Aliguliyev, 2009a, 2010; Wang et al., 2011).”
Short Text Similarity with Word Embeddings (2015, background): “Being able to do so successfully is beneficial in many settings in information retrieval like search [26], query suggestion [30], automatic summarization [3] and image finding [12].”
Recent automatic text summarization techniques: a survey (2016, methodology): “This method uses a memetic algorithm which has combined the population based search of evolutionary algorithm with a guided local search strategy DE (Aliguliyev 2009) A method named as differential evolution is implemented in this approach which optimizes the allocation of each sentence to a group.”