CHUQUR O‘QITISH USULLARI ASOSIDA MATNLI MA’LUMOTLARNING QISQACHA MAZMUNINI CHIQARISH METODLARI
Keywords:
Kalit so’zlat: Frequency-Based Extractive Summarization, Heuristik algoritm, Statistik yondashuv, TextRank, SumBasic, BERT, T5, Transformer modellar.Abstract
Annotatsiya: Ushbu ishda chuqur o‘qitish usullari asosida matnli ma’lumotlarning qisqacha mazmunini avtomatik tarzda chiqarish algoritmini ishlab chiqish masalasi ko‘rib chiqilgan. Bugungi kunda katta hajmdagi matnli axborotni qisqa, aniq va mazmunli shaklga keltirishga bo‘lgan ehtiyoj ortib bormoqda. Ayniqsa, yangiliklar, ilmiy maqolalar, hujjatlar va boshqa matnli manbalarni tezkor tahlil qilishda bunday texnologiyalarning roli katta ahamiyat kasb etadi. Ish natijasida matnlarning mazmunini tushunarli va ixcham shaklda chiqarib bera oladigan algoritm ishlab chiqildi.
References
1. Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural Machine Translation by Jointly Learning to Align and Translate. arXiv preprint arXiv:1409.0473.
2. Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention is All You Need. Advances in Neural Information Processing Systems (NeurIPS).
3. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805.
4. Raffel, C., Shazeer, N., Roberts, A., et al. (2020). Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. Journal of Machine Learning Research, 21(140), 1–67.
5. Nallapati, R., Zhai, F., & Zhou, B. (2016). Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond. arXiv preprint arXiv:1602.06023.