Preliminary report on network based keyword extraction for Croatian is an unsupervised method for keyword extraction from the complex network. We build our approach with a new network measure the node selectivity, motivated by the research of the graph based centrality approaches. The node selectivity is defined as the average weight distribution on the links of the single node. We extract nodes (keyword candidates) based on the selectivity value. Furthermore, we expand extracted nodes to word-tuples ranked with the highest in out selectivity values. Selectivity based extraction does not require linguistic knowledge while it is purely derived from statistical and structural information en-compassed in the source text which is reflected into the structure of the network. Obtained sets are evaluated on a manually annotated keywords: for the set of extracted keyword candidates average F1 score is 24,63 , and average F2 score is 21,19 ; for the exacted words-tuples candidates average F1 score is 25,9 and average F2 score is 24,47 .
Mihalcea and Tarau @cite_5 report a seminal research which introduced state-of-the-art TextRank model. TextRank is derived from PageRank and introduced to graph based text processing, keyword and sentence extraction. Abstracts are modelled as undirected or directed and weighted co-occurrence networks using the co-occurrence window of variable sizes (2..10). Lexical units are preprocessed: stopwords removed, words restricted with POS syntactic filters (open class words, nouns and adjectives, nouns). The PageRank motivated score of importance of the node derived from importance of the neighboring nodes is used for keyword extraction. Obtained TextRank performance compares favorably with the supervised machine learning n-gram based approach.
@cite_5
2110693578 1525391233 2962984063 2146769536
We introduce a stochastic graph-based method for computing relative importance of textual units for Natural Language Processing. We test the technique on the problem of Text Summarization (TS). Extractive TS relies on the concept of sentence salience to identify the most important sentences in a document or set of documents. Salience is typically defined in terms of the presence of particular important words or in terms of similarity to a centroid pseudo-sentence. We consider a new approach, LexRank, for computing sentence importance based on the concept of eigenvector centrality in a graph representation of sentences. In this model, a connectivity matrix based on intra-sentence cosine similarity is used as the adjacency matrix of the graph representation of sentences. Our system, based on LexRank ranked in first place in more than one task in the recent DUC 2004 evaluation. In this paper we present a detailed analysis of our approach and apply it to a larger data set including data from earlier DUC evaluations. We discuss several methods to compute centrality using the similarity graph. The results show that degree-based methods (including LexRank) outperform both centroid-based methods and other systems participating in DUC in most of the cases. Furthermore, the LexRank with threshold method outperforms the other degree-based techniques including continuous LexRank. We also show that our approach is quite insensitive to the noise in the data that may result from an imperfect topical clustering of documents. The selection of the most descriptive terms or passages from text is crucial for several tasks, such as feature extraction and summarization. In the majority of the cases, research works propose the ranking of all candidate keywords or sentences and then select the top-ranked items as features, or as a text summary respectively. Ranking is usually performed using statistical information from text (i.e., frequency of occurrence, inverse document frequency, co-occurrence information). In this paper we present SemanticRank, a graph-based ranking algorithm for keyword and sentence extraction from text. The algorithm constructs a semantic graph using implicit links, which are based on semantic relatedness between text nodes and consequently ranks nodes using different ranking algorithms. Comparative evaluation against related state of the art methods for keyword and sentence extraction shows that SemanticRank performs favorably in previously used data sets. Abstract Motivated by the success of powerful while expensive techniques to recognize words in a holistic way (, 2013; , 2014; , 2016) object proposals techniques emerge as an alternative to the traditional text detectors. In this paper we introduce a novel object proposals method that is specifically designed for text. We rely on a similarity based region grouping algorithm that generates a hierarchy of word hypotheses. Over the nodes of this hierarchy it is possible to apply a holistic word recognition method in an efficient way. Our experiments demonstrate that the presented method is superior in its ability of producing good quality word proposals when compared with class-independent algorithms. We show impressive recall rates with a few thousand proposals in different standard benchmarks, including focused or incidental text datasets, and multi-language scenarios. Moreover, the combination of our object proposals with existing whole-word recognizers (, 2014; , 2016) shows competitive performance in end-to-end word spotting, and, in some benchmarks, outperforms previously published results. Concretely, in the challenging ICDAR2015 Incidental Text dataset, we overcome in more than 10 F -score the best-performing method in the last ICDAR Robust Reading Competition (Karatzas, 2015). Source code of the complete end-to-end system is available at https: github.com lluisgomez TextProposals . This paper explores several unsupervised approaches to automatic keyword extraction using meeting transcripts. In the TFIDF (term frequency, inverse document frequency) weighting framework, we incorporated part-of-speech (POS) information, word clustering, and sentence salience score. We also evaluated a graph-based approach that measures the importance of a word based on its connection with other sentences or words. The system performance is evaluated in different ways, including comparison to human annotated keywords using F-measure and a weighted score relative to the oracle system performance, as well as a novel alternative human evaluation. Our results have shown that the simple unsupervised TFIDF approach performs reasonably well, and the additional information from POS and sentence score helps keyword extraction. However, the graph method is less effective for this domain. Experiments were also performed using speech recognition output and we observed degradation and different patterns compared to human transcripts.