Personalized Digest Generator for Muted WhatsApp Groups Using NLP and Weighted Importance Scoring

This paper presented a Personalized Digest Generator for muted WhatsApp groups, addressing the dual challenges of information overload and absent user-level personalization in existing chat summarization systems. The proposed multi-weighted Importance Scoring Function jointly considers keyword relevance, sender authority, message length, and a noise penalty, calibrated against an empirically validated threshold of 0.35. This function demonstrably outperforms authority-only and keyword-only baselines by explicitly penalizing high-authority, low-content messages. The modular eight-stage pipeline design enables independent upgradability of each component, and the on-device processing architecture provides strong privacy guarantees without sacrificing summarization quality. Future work will address three limitations: (1) integration with the WhatsApp Business API for real-time automated delivery; (2) replacement of the rule-based preference filter with a learned user-interest model trained on interaction history; and (3) multilingual evaluation using SBERT multilingual variants, extending the multilingual summarization work surveyed by Kumar et al. [9].

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