--- a
+++ b/wrapper_functions/summarization_functions.py
@@ -0,0 +1,71 @@
+from transformers import pipeline
+from summa.summarizer import summarize
+
+def get_single_summary(text, model_name="t5-small", min_length=50, max_length=200):
+    '''
+    https://huggingface.co/transformers/v3.0.2/_modules/transformers/pipelines.html#SummarizationPipeline
+    :param text: input sequence, a string or a list of string
+    :param model_name: model_name: "bart-large-cnn", "t5-small", "t5-base", "t5-large", "t5-3b", "t5-11b" and "razent/SciFive-large-Pubmed_PMC", other models https://huggingface.co/models?sort=downloads&search=summarization
+    :param min_length: min length in summary
+    :param max_length: max length in summary
+    :return: a list of string
+    '''
+    # choices: '`bart-large-cnn`', '`t5-small`', '`t5-base`', '`t5-large`', '`t5-3b`', '`t5-11b`'
+    classifier = pipeline("summarization",model=model_name,tokenizer=model_name)
+    res = classifier(text,min_length=min_length,max_length=max_length)
+    final_summary = []
+    for summary in res:
+        final_summary.append(summary['summary_text'])
+    return final_summary
+
+
+def get_multi_summary_joint(text, model_name="osama7/t5-summarization-multinews", min_length=50, max_length=200):
+    '''
+    Join all the input documents as a long document, then do single document summarization
+    :param text: input sequence, a string or a list of string
+    :param model_name: model_name:  "bart-large-cnn", "t5-small", "t5-base", "t5-large", "t5-3b", "t5-11b" and "razent/SciFive-large-Pubmed_PMC", other models https://huggingface.co/models?sort=downloads&search=summarization
+    :param min_length: min length in summary
+    :param max_length: max length in summary
+    :return: a list of string
+    '''
+    # choices: '`bart-large-cnn`', '`t5-small`', '`t5-base`', '`t5-large`', '`t5-3b`', '`t5-11b`'
+    classifier = pipeline("summarization", model=model_name, tokenizer=model_name)
+    text = ' '.join(text)
+    res = classifier(text, min_length=min_length, max_length=max_length)
+    final_summary = []
+    for summary in res:
+        final_summary.append(summary['summary_text'])
+    return final_summary
+
+
+
+def get_multi_summary_extractive_textRank(text,ratio=-0.1,words=0):
+    '''
+    Textrank method for multi-doc summarization
+    :param text: a list of string
+    :param ratio: the ratio of summary (0-1.0)
+    :param words: the number of words of summary, default is 50
+    :return: a string as the final summary
+    
+    Example for testing:
+    >>text1 = 'Automatic summarization is the process of reducing a text document with a \
+    computer program in order to create a summary that retains the most important points \
+    of the original document. As the problem of information overload has grown, and as \
+    the quantity of data has increased, so has interest in automatic summarization.'
+    >>text2 = 'Technologies that can make a coherent summary take into account variables such as \
+    length, writing style and syntax. An example of the use of summarization technology \
+    is search engines such as Google. Document summarization is another.'
+    >>print(get_multi_summary_extractive_textRank([text1,text2]))
+    '''
+    text = ' '.join(text)
+    summ = None
+    if ratio>0:
+        summ = summarize(text, ratio=ratio)
+
+    if words>0:
+        summ = summarize(text, words=words)
+
+    if summ is None:
+        summ = summarize(text, words=50)
+
+    return summ.replace('\n',' ')