Meeting Summarizer Using NLP

References

[1] Kongthon, Alisa; Sangkeettrakarn, Chatchawal; Kongyoung, Sarawoot; Haruechaiyasak, Choochart (October 27–30, 2009). Implementing an online help desk system based on conversational agents. MEDES '09: The International Conference on Management of EmergentDigitalEcoSystems.France: ACM. doi:10.1145/1643823.1643908.

[2] Mitchell, Tom (1997). Machine Learning. New York: McGraw Hill. ISBN 0-07-042807-7. OCLC 36417892.

[3] A. P. Singh, R. Nath and S. Kumar, "A Survey: Speech Recognition Approaches and Techniques," 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering(UPCON),2018,pp.1-4, doi:10.1109/UPCON.2018.8596954.

[4] N. Chumuang and M. Ketcham, "Model for Handwritten Recognition Based on Artificial Intelligence," 2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI- NLP), 2018, pp. 1-5, doi: 10.1109/iSAI-NLP.2018.8692958.

[5] J. N. Madhuri and R. Ganesh Kumar, "Extractive Text Summarization Using Sentence Ranking," 2019 International Conference on Data Science and Communication (IconDSC), 2019, pp. 1-3, doi: 10.1109/IconDSC.2019.8817040.

[6] S. S. Desai, D. Rajput and K. Patil, "An approach for Text Recognition from Document Images," 2020 IEEE Bangalore Humanitarian Technology Conference (B-HTC), 2020, pp. 1-5, doi: 10.1109/B-HTC50970.2020.9297939.

[7] Du, Y. et al. (2020) “PP-OCR: A practical ultra-lightweight OCR

[8] system,” arXiv [cs.CV]. doi: 10.48550/ARXIV.2009.09941.

[9] Ghadage, Y. H. and Shelke, S. D. (2016) “Speech to text conversion for multilingual languages,” in 2016 International Conference on Communication and Signal Processing (ICCSP). IEEE, pp. 0236– 0240.

[10] Jo, T. (2017) “K nearest neighbor for text summarization using feature similarity,” in 2017 International Conference on Communication, Control, Computing and Electronics Engineering (ICCCCEE). IEEE, pp. 1–5.

[11] Jolad, B. and Khanai, R. (2019) “An Art of Speech Recognition: A Review,” in 2019 2nd International Conference on Signal Processing and Communication (ICSPC). IEEE, pp. 31–35.

[12] Lakkhanawannakun, P. and Noyunsan, C. (2019) “Speech Recognition using Deep Learning,” in 2019 34th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC). IEEE, pp. 1–4.

[13] Ozsoy, M. G., Alpaslan, F. N. and Cicekli, I. (2011) “Text summarization using Latent Semantic Analysis,” Journal of information science, 37(4), pp. 405–417. doi: 10.1177/0165551511408848.

[14] Raundale, P. and Shekhar, H. (2021) “Analytical study of Text Summarization Techniques,” in 2021 Asian Conference on Innovation in Technology (ASIANCON). IEEE, pp. 1–4.

[15] Sharma, N. and Sardana, S. (2016) “A real time speech to text conversion system using bidirectional Kalman filter in Matlab,” in 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, pp. 2353–2357.

[16] Singh, S. S. and Karayev, S. (2021) “Full page handwriting recognition via image to sequence extraction,” in Document Analysis and Recognition – ICDAR 2021. Cham: Springer International

@article{1712104,
      author = {Lekhana R, Nishath Anjum, Syed Saqeeb, Thanushree B S, Irfan Khan},
      title = {Meeting Summarizer Using NLP},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {4},
      pages = {1975-1981},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712104.pdf},
      abstract = {This project proposes a hybrid Natural Language Processing (NLP) system for automating meeting summarization and action item extraction. The system converts meeting audio into text using speech-to-text technology, preprocesses the data, and applies a combination of extractive and abstractive summarization techniques utilizing transformer models such as T5 and BART. Additionally, it extracts critical action items including tasks, deadlines, and responsible individuals through named entity recognition and dependency parsing. The designed web-based platform enhances productivity by delivering clear, concise meeting summaries and structured follow-up actions. Evaluation on real and synthetic datasets demonstrates improved accuracy and effectiveness over traditional methods, making it a valuable tool for efficient organizational communication.},
      keywords = {Meeting summarization, Natural language processing, Extractive summarization, Abstractive summarization, Action item extraction, Speech-to-text, Transformer models, Named entity recognition, Hybrid NLP system, Organizational productivity.},
      month = {October},
      doi = {https://doi.org/10.64388/IREV9I4-1712104}
  }