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@@ -50,7 +50,7 @@ The following is a summary of the commonly used NLP scenarios covered in the rep
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 |Text Classification                     |BERT, XLNet, RoBERTa| Text classification is a supervised learning method of learning and predicting the category or the class of a document given its text content. |English, Hindi, Arabic|
 |Named Entity Recognition                |BERT| Named entity recognition (NER) is the task of classifying words or key phrases of a text into predefined entities of interest. |English|
-|Text Summarization|BERTSumExt <br> BERTSumAbs <br> UniLM (s2s-ft)|Text summarization is a language generation task of summarizing the input text into a shorter paragraph of text.|English
+|Text Summarization|BERTSumExt <br> BERTSumAbs <br> UniLM (s2s-ft) <br> MiniLM |Text summarization is a language generation task of summarizing the input text into a shorter paragraph of text.|English
 |Entailment                              |BERT, XLNet, RoBERTa| Textual entailment is the task of classifying the binary relation between two natural-language texts,  *text* and *hypothesis*, to determine if the *text* agrees with the *hypothesis* or not. |English|
 |Question Answering                      |BiDAF, BERT, XLNet| Question answering (QA) is the task of retrieving or generating a valid answer for a given query in natural language, provided with a passage related to the query. |English|
 |Sentence Similarity                     |BERT, GenSen| Sentence similarity is the process of computing a similarity score given a pair of text documents. |English|