translation: Update summary.md

rewritten some parts of the sentence as per Thomas suggested.
please note that some parts I re-wrote it a bit differently.

en/docs/chapter_searching/summary.md

@@ -1,8 +1,8 @@
 # Summary
 
 - Binary search depends on the order of data and performs the search by iteratively halving the search interval. It requires the input data to be sorted and is only applicable to arrays or array-based data structures.
-- Brute force search locates data by traversing the data structure. Linear search is suitable for arrays and linked lists, while breadth-first search (BFS) and depth-first search (DFS) are suitable for graphs and trees. These algorithms are highly versatile, requiring no preprocessing of data, but have a higher time complexity of $O(n)$.
+- Brute force search may be required to locate an entry in an unordered dataset. Different search algorithms can be applied based on the data structure: Linear search is suitable for arrays and linked lists, while breadth-first search (BFS) and depth-first search (DFS) are suitable for graphs and trees. These algorithms are highly versatile, requiring no preprocessing of data, but have a higher time complexity of $O(n)$.
-- Hash search, tree search, and binary search are efficient searching methods that quickly locate target elements within specific data structures. These algorithms are highly efficient, with time complexities reaching $O(\log n)$ or even $O(1)$, but they usually require additional data structures.
+- Hash search, tree search, and binary search are efficient search methods that can quickly locate target elements within specific specific data structures. These algorithms are highly efficient, with time complexities reaching $O(\log n)$ or even $O(1)$, but they usually require extra space to accommodate additional data structures.
-- In practice, we need to analyze factors such as data volume, search performance requirements, data query and update frequencies, etc., to select an appropriate search method.
+- In practice, we need to analyze factors such as data volume, search performance requirements, data query and update frequencies, etc., to choose an appropriate search method.
-- Linear search is suitable for small or frequently updated data; binary search is suitable for large, sorted data; hash search is suitable for scenarios requiring high query efficiency without the need for range queries; tree search is appropriate for large dynamic data that needs to maintain order and support range queries.
+- Linear search is ideal for small or frequently updated (volatile) data. Binary search works well for large, sorted datasets. Hash search is suitable when high query efficiency is needed without range queries. Tree search is best suited for large, dynamic datasets where maintaining order and supporting range queries is important.
-- Replacing linear search with hash search is a common strategy to optimize runtime, reducing the time complexity from $O(n)$ to $O(1)$.
+- Replacing linear search with hash search is a common strategy to optimize runtime performance, reducing the time complexity from $O(n)$ to $O(1)$.