Hukuk Metinleri için Anahtar Kelime Kullanımı ile Otomatik Özetleme

Year 2021, Volume: 1 Issue: 1, 24 - 36, 30.06.2021

Erol Gödur

Abstract

Mahkemelerde, benzer davalar için önceki mahkemelerin verdiği kararlar mevcut mahkemenin karar verme sürecinde bağlayıcı bir etkiye sahiptir. Bu nedenle avukatlar yer aldıkları davalar ile benzerlik içeren davalara ve sonuçlara ulaşmak için araştırma yaparlar. Hukuki metinlerde aranan duruma ilişkin farklı emsal kararların varlığı, kullanıcının yaptığı aramaya göre birden fazla mahkeme sonuç metninin incelenmesini gerektirir. Metin sayısının fazla olması ve metinlerin yazı miktarlarının büyük olması, kullanıcı tarafından önemli bir zaman ve emek harcanmasına neden olur. Kararlarda özet metinlerin kullanılması, bu süreyi ve çabayı daha makul bir kullanım düzeyine indirecektir. Bu nedenle, bu çalışma, bu yasal metinler için otomatik bir özetleme sistemi geliştirmeyi amaçlamaktadır. Bu amaçla özet çıkarımı ile anahtar kelime frekans temelli özetleme, yönsüz çizge kullanımı ile özet çıkarma, ağırlıklı çizge kullanımı ile özet çıkarımı ve frekans temelli özetleme ile çizge tabanlı özetlemenin hibrit olarak kullanıldığı karma model uygulanmıştır. Bu modellerden en başarılısı karma model olarak belirlenmiştir.

Keywords

Doğal dil işleme, Otomatik özet çıkarımı, Hukuki metinler

Automatic Summarization with Keyword for Legal Texts

Abstract

In legal cases, the decisions made by the previous courts for similar cases affect the determination of current courts. Therefore, lawyers do research to reach similar cases and results. The existence of different precedent decisions regarding the situation sought in the legal texts requires the examination of more than one court result text according to the search made by the user. The high number of texts and the high size of the texts cause a significant time and effort to open by the user. The use of summary texts for decisions will reduce this time and effort to a more reasonable level of use. Therefore, this study aim to develop an automatic summarization system for these legal texts. For this purpose, frequency base, undirected graph base, weighted graph base summarization extraction methods have applied. In addition, a hybrid method that consists frequency base and weighted graph base summarization has applied. This method has best results.

Keywords

Natural language processing, Automatic summarization, Legal texts

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