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Fine-tuning and multilingual pre-training for abstractive summarization task for the Arabic language
Full text linkThe main task of our research is to train various abstractive summarization models for the Arabic language. The work for abstractive Arabic
text summarization has hardly begun so far due to the unavailability of the
datasets needed for that. In our previous research, we created the first monolingual corpus in the Arabic language for abstractive text summarization.
Based on this corpus, we fine-tuned various transformer models. We tested
the PreSumm and multilingual BART models. We achieved a “state of the
art” result in this area with the PreSumm method.
The present study continues the same series of research. We extended
our corpus “AraSum” and managed to reach up to 50 thousand items, each
consisting of an article and its corresponding lead. In addition, we pretrained our own monolingual and trilingual BART models for the Arabic
language and fine-tuned them in addition to the mT5 model for abstractive
text summarization for the same language, using the AraSum corpus. While
there is room for improvement in the resources and the infrastructure we
possess, the results clearly demonstrate that most of our models surpassed
the XL-Sum which is considered to be state of the art for abstractive Arabic
text summarization so far. Our corpus “AraSum” will be released to facilitate
future work on abstractive Arabic text summarization
„Mint színi hatást nem igérő, visszautasitatott.” − A Nemzeti Színház drámabíráló bizottsága az 1840-es évek második felében
Full text linkBenchmarking Redis and HBase NoSQL Databases using Yahoo Cloud Service Benchmarking tool
Full text linkThe Not Structured Query Language (NoSQL) databases have
become more relevant to applications developers as the need for scalable
and flexible data storage for online applications has increased. Each NoSQL
database system provides features that fit particular types of applications.
Thus, the developer must carefully select according to the application’s needs.
Redis is a key-value NoSQL database that provides fast data access. On the
other hand, the Apache HBase database is a column-oriented database that
offers scalability and fast data access, is a promising alternative to Redis in
some types of applications. In this research paper, the goal is to use the
Yahoo Cloud Serving Benchmark (YCSB) to compare the performance of
two databases (Redis and HBase). The YCSB platform has been developed
to determine the throughput of both databases against different workloads.
This paper evaluates these NoSQL databases with six workloads and varying
threads
Fine-tuning and multilingual pre-training for abstractive summarization task for the Arabic language
Full text linkThe main task of our research is to train various abstractive summarization models for the Arabic language. The work for abstractive Arabic
text summarization has hardly begun so far due to the unavailability of the
datasets needed for that. In our previous research, we created the first monolingual corpus in the Arabic language for abstractive text summarization.
Based on this corpus, we fine-tuned various transformer models. We tested
the PreSumm and multilingual BART models. We achieved a “state of the
art” result in this area with the PreSumm method.
The present study continues the same series of research. We extended
our corpus “AraSum” and managed to reach up to 50 thousand items, each
consisting of an article and its corresponding lead. In addition, we pretrained our own monolingual and trilingual BART models for the Arabic
language and fine-tuned them in addition to the mT5 model for abstractive
text summarization for the same language, using the AraSum corpus. While
there is room for improvement in the resources and the infrastructure we
possess, the results clearly demonstrate that most of our models surpassed
the XL-Sum which is considered to be state of the art for abstractive Arabic
text summarization so far. Our corpus “AraSum” will be released to facilitate
future work on abstractive Arabic text summarization