Improvement of an abstractive summarization evaluation tool using lexical-semantic relations and weighted syntax tags in Farsi language

In recent years, high increase in the amount of published web elements and the need to store, classify, restore, and process them have intensified the importance of natural language processing and its related tools such as automatic summarizers and machine translators. In this paper, a novel approach for evaluating automatic abstractive summarization system is proposed which can also be used in the other Natural Language Processing and Information Retrieval Applications. By comparing auto-abstracts (abstracts created by machine) with human abstracts (ideal abstracts created by human), the metrics introduced in the proposed tool can automatically measure the quality of auto-abstracts. Evidently, we can't semantically compare texts of abstractive summaries by comparison of just their words' appearance. So it is necessary to use a lexical database such as WordNet. We use FerdowsNet with a proper idea for Farsi language and it notably improves the evaluation results. This tool has been assessed by linguistic experts. This tool contains metric for determining the quality of summaries automatically by comparing them with summaries generated by humans (Ideal summaries). Evidently, we can't semantically compare texts of abstractive summaries by comparison of just their words' appearance and it is necessary to use a lexical database. We use this database with a proper idea together with Farsi parser in order to identify groups forming sentences and the results of evaluation improve significantly.