In this paper, we evaluate our automatic text summarization system in multilingual context. We participated in both single document and multi-document summarization tasks of MultiLing 2015 workshop. Our method involves clustering the document sentences into topics using a fuzzy clustering algorithm. Then each sentence is scored according to how well it covers the various topics. This is done using statistical features such as TF, sentence length, etc. Finally, the summary is constructed from the highest scoring sentences, while avoiding overlap between the summary sentences. This makes it language-independent, but we have to afford preprocessed data first (tokenization, stemming, etc.).
Statistical extractive summarization is one of the most exploited approach in automatic text summarization due to its generation speed, implementation easiness and multilingual property. We want to improve statistical sentence scoring by exploring a simple, yet powerful, property of graphs called bushy paths represented by the number of node's neighbors. A graph of similarities is constructed in order to select candidate sentences. Statistical features such as sentence position, sentence length, term frequency and sentences similarities are used to get a primary score for each candidate sentence. The graph is used again to enhance the primary score by using bushy paths property. Also, we tried to exploit the graph in order to enhance summary's coherence. We experimented our method using MultiLing'15 workshop's corpora for multilingual single document summarization. Using graph properties can improve statistical scoring without loosing the multilingualism of the method.
The representation of sentences is a very important task. It can be used as a way to exchange data interapplications. One main characteristic, that a notation must have, is a minimal size and a representative form. This can reduce the transfer time, and hopefully the processing time as well.Usually, sentence representation is associated to the processed language. The grammar of this language affects how we represent the sentence. To avoid language-dependent notations, we have to come up with a new representation which don't use words, but their meanings. This can be done using a lexicon like wordnet, instead of words we use their synsets. As for syntactic relations, they have to be universal as much as possible.Our new notation is called STON "SenTences Object Notation", which somehow has similarities to JSON. It is meant to be minimal, representative and language-independent syntactic representation. Also, we want it to be readable and easy to be created. This simplifies developing simple automatic generators and creating test banks manually. Its benefit is to be used as a medium between different parts of applications like: text summarization, language translation, etc. The notation is based on 4 languages: Arabic, English, Franch and Japanese; and there are some cases where these languages don't agree on one representation. Also, given the diversity of grammatical structure of different world languages, this annotation may fail for some languages which allows more future improvements.
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