Skorkovská, Lucie

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91JMZW: Application of Summarization Method in the TopicIdentification of Czech Newspaper ArticlesLucie Skorkovska´11 IntroductionThe topic identification module, which is a part of a complex system for acquisition andstoring large volumes of text data (Sˇvec et al. (2011)), processes each acquired data item andassigns to it topics from a defined topic hierarchy. The topic hierarchy is quite extensive - itcontains about 450 topics and topic categories. Since the system is used for processing largeamounts of data, a summarization method was implemented and the effect of using only thesummary of an article on the topic identification accuracy is studied.The main purpose of the topic identification module is to filter the huge amount of dataaccording to their topics for the future use as the language modeling training data. The moduleuses a language modeling based approach similar to the Naive Bayes classifier for the imple-mentation of the topic identification and assigns 3 topics to each article. Topics are chosen froma hierarchical system - a “topic tree”. Further information about the topic identification modulecan be found in Skorkovska´ et al. (2011).2 Summarization moduleFor the automatic summarization module an extractive generic summarization was cho-sen, as we want our summaries to preserve all the information contained in the original text, sothe topic identification module can assign the correct topics. The implemented summarizationalgorithm selects the most important sentences in a text, where an importance of a sentence ismeasured by the importance of its words. One of the most commonly used measure for assess-ing the word importance in information retrieval area is the TF-IDF measure (Term Frequency- Inverse Document Frequency), so we have decided to use it as well. The summary is createdin a following way:1. Split text to sentences and sentences to words.2. For each term t in the document compute an idf weight:idft = logNNt(1)where N is the total number of sentences in the document and Nt is the number of sen-tences containing the term t.3. For each sentence s compute a term frequency tft,s for each term. We have used thenormalization of the term frequency by the maximum term frequency in the sentence.4. The importance score S of each sentence in the document is computed as:Ss =∑t∈stft,s · idft (2)1 student of the doctoral study programme Applied Sciences and Informatics, specialization Cybernetics, e-mail: lskorkov@kky.zcu.cz925. The five sentences with the highest score S are included in the summary.3 EvaluationFor the experiments three smaller collections containing 5000, 10000 and 31419 arti-cles were separated from the whole corpus. Evaluation from the point of view of informationretrieval (IR) was performed on the collections, where each newly downloaded article is consid-ered as a query in IR and precision (P ), recall (R) and F1-measure is computed for the answertopic set. The results can be seen in table 1. For the comparison the collections trained alsousing the preprocessing with the lemmatization module are shown.Table 1: Results of topic identification on different collectionscoll./art. word lemma summary lemma/summary word/summary5k P 0.5366 0.5547 0.5028 0.5457 0.5374R 0.5544 0.5754 0.5155 0.5686 0.5546F1 0.5454 0.5649 0.5091 0.5569 0.545910k P 0.5481 0.5536 0.5024 0.5378 0.5293R 0.5472 0.5555 0.4979 0.5421 0.53F1 0.5476 0.5546 0.5002 0.54 0.529630k P 0.5864 0.5859 0.5387 0.5588 0.5598R 0.6125 0.6155 0.5616 0.5921 0.5884F1 0.5992 0.6003 0.5499 0.575 0.57374 ConclusionFrom the table we can draw following conclusions: first, the summarized text is notsuitable for training topic identification statistics - results in column summary are the worst forall sizes of collections. This is not surprising, as much less text is used for counting the statisticsso the topic important words may be missing.On the other hand, interesting finding can be seen in column lemma/summary. Whenneeded, a faster computation of topic identification using summarized and lemmatized texts canbe used with a minimum loss on the topic identification accuracy. The time needed for the topicidentification of an article is reduced as the computation of the probability P (T |A) of an articlebelonging to a topic is done over a reduced set of words.AcknowledgementThe work has been supported by the grant of The University of West Bohemia, projectNo. SGS-2010-054.ReferencesSkorkovska´, L., Ircing, P., Prazˇa´k, A., Lehecˇka, J., 2011. Automatic topic identification forlarge scale language modeling data filtering. Text, Speech and Dialogue, Lecture Notes inComputer Science, vol. 6836, pp. 64–71. Springer Berlin / HeidelbergSˇvec, J., Hoidekr, J., Soutner, D., Vavrusˇka, J., 2011. Web text data mining for building largescale language modelling corpus. Text, Speech and Dialogue, Lecture Notes in ComputerScience, vol. 6836, pp. 356–363. Springer Berlin / Heidelberg

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JMZW: application of summarization method in the topic identification of czech newspaper articles

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JMZW: application of summarization method in the topic identification of czech newspaper articles

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