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Extractive summarization is an important natural language processing approach used for document compression, improved reading comprehension, key phrase extraction, indexing, query set generation, and other analytics approaches. Extractive summarization has specific advantages over abstractive summarization in that it preserves style, specific text elements, and compound phrases that might be more directly associated with the text. In this article, the relative effectiveness of extractive summarization is considered on two widely different corpora: (1) a set of works of fiction (100 total, mainly novels) available from Project Gutenberg, and (2) a large set of news articles (3000) for which a ground truthed summarization (gold standard) is provided by the authors of the news articles. Both sets were evaluated using 5 different Python Sumy algorithms and compared to randomly-generated summarizations quantitatively. Two functional approaches to assessing the efficacy of summarization using a query set on both the original documents and their summaries, and using document classification on a 12-class set to compare among different summarization approaches, are introduced. The results, unsurprisingly, show considerable differences consistent with the different nature of these two data sets. The LSA and Luhn summarization approaches were most effective on the database of fiction, while all five summarization approaches were similarly effective on the database of articles. Overall, the Luhn approach was deemed the most generally relevant among those tested. [ABSTRACT FROM AUTHOR]

A novel method is presented for evaluating the efficacy of object recognition algorithms on occluded images, called the occluded image function (OIF). The OIF describes system behavior in occluded environments and thus gives qualitative insight into their mechanisms; derivative metrics from OIF can also be used to quantitatively compare classifiers. To showcase the utility of the OIF, an experiment is performed by obstructing optical gait images from two biped robot models and using four binary machine learning classifiers to distinguish between them. The OIF diagrams are created from each experiment, and the resulting insights about the classifiers are discussed. Using the OIF, it is shown that the primitive classifiers can sometimes perform better under occlusion conditions, possibly due to pre-filtering of gait data by uniform occlusions. This result serves to demonstrate that the OIF is a useful tool for classifier evaluation. [ABSTRACT FROM AUTHOR]

Paraphrase identification consists in the process of verifying if two sentences are semantically equivalent or not. It is applied in many natural language tasks, such as text summarization, information retrieval, text categorization, and machine translation. In general, methods for assessing paraphrase identification perform three steps. First, they represent sentences as vectors using bag of words or syntactic information of the words present the sentence. Next, this representation is used to measure different similarities between two sentences. In the third step, these similarities are given as input to a machine learning algorithm that classifies these two sentences as paraphrase or not. However, two important problems in the area of paraphrase identification are not handled: (i) the meaning problem: two sentences sharing the same meaning, composed of different words; and (ii) the word order problem: the order of the words in the sentences may change the meaning of the text. This paper proposes a paraphrase identification system that represents each pair of sentence as a combination of different similarity measures. These measures extract lexical, syntactic and semantic components of the sentences encompassed in a graph. The proposed method was benchmarked using the Microsoft Paraphrase Corpus, which is the publicly available standard dataset for the task. Different machine learning algorithms were applied to classify a sentence pair as paraphrase or not. The results show that the proposed method outperforms state-of-the-art systems. [ABSTRACT FROM AUTHOR]