Comparative Study: Word Frequency, K-Means & PageRank for Arabic Summarization
Systematic performance and computational latency benchmark comparing frequency, K-Means, and PageRank for Arabic news condensation.
The situation & problem
Engineering teams deploying text summarization in production need clear latency vs semantic coverage trade-offs before provisioning expensive GPU servers.
The operational constraints
Evaluating disparate unsupervised algorithmic families against diverse Arabic document structures with standardized stopword and stemmer pipelines.
Architectural tradeoffs & what was refused
Evaluating algorithms on only one dataset slice without latency and memory profiling.
What I built & shipped
A comprehensive empirical framework and benchmark suite evaluating extraction fidelity, sentence diversity, and runtime resource utilization.
Architecture & stack
Python, Scikit-learn, NetworkX, NLTK, Tashaphyne Arabic Light Stemmer, Pandas.
Result & measurable impact
Established actionable decision matrices for selecting the optimal low-compute algorithm depending on latency thresholds and text length.
Next iterations & roadmap
Hybrid ensemble pipeline combining graph centrality with lightweight sentence embeddings.
System surfaces & interface captures
Hover and click any record to open the full-resolution pixel inspector with zoom lens and 1:1 precision.
Have a similar constraint?
Tell me where your current operational system or product is breaking.