[NSE-330]Refine Readme introduction (#331)

README.md

@@ -14,7 +14,11 @@ You can find the all the Native SQL Engine documents on the [project web page](h
 
 ![Overview](./docs/image/nativesql_arch.png)
 
-Spark SQL works very well with structured row-based data. It used WholeStageCodeGen to improve the performance by Java JIT code. However Java JIT is usually not working very well on utilizing latest SIMD instructions, especially under complicated queries. [Apache Arrow](https://arrow.apache.org/) provided CPU-cache friendly columnar in-memory layout, its SIMD optimized kernels and LLVM based SQL engine Gandiva are also very efficient. Native SQL Engine used these technologies and brought better performance to Spark SQL.
+Spark SQL works very well with structured row-based data. It used WholeStageCodeGen to improve the performance by Java JIT code. However Java JIT is usually not working very well on utilizing latest SIMD instructions, especially under complicated queries. [Apache Arrow](https://arrow.apache.org/) provided CPU-cache friendly columnar in-memory layout, its SIMD-optimized kernels and LLVM-based SQL engine Gandiva are also very efficient.
+
+Native SQL Engine reimplements Spark SQL execution layer with SIMD-friendly columnar data processing based on Apache Arrow, 
+and leverages Arrow's CPU-cache friendly columnar in-memory layout, SIMD-optimized kernels and LLVM-based expression engine to bring better performance to Spark SQL.
+
 
 ## Key Features