Hi Raja Kumar, Thanks for reading.
Yes, both the Algorithms has produced a different result for my text.
In general, TF-IDF is used when you are summarizing multiple documents, TF-IDF finds the important words to be included in the final summary even if their total frequency is very less.
See the definition here,
“A High weight in TF-IDF is reached by a high term frequency(in the given document) and a low document frequency of the term in the whole collection of documents.”
i.e, take the example of the word `Italy` is discussed multiple times in an article about the corona virus. The same word is not having much frequency is other documents sampled for the summarization. TF-IDF would still mark `Ìtaly` as an important word.
On the other hand, WordFrequency Algorithm marks the word as important based on their total frequency in all the sampled text.
i.e, the word `corona` or `china` would be the most frequent words in all the articles related to corona virus.
In general, TF-IDF is suitable for multiple-document summarization where we need to cover more details in summarization. Wherein WordFrequency is used for single-document summarization.
Hope this clear doubts.