diff --git a/.gitignore b/.gitignore
index 1d3a133f..b443d97d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -8,4 +8,5 @@ server/coverage
 # Mac specific files ignored.
 extensions/.DS_Store
 extensions/css/.DS_Store
-.DS_Store
\ No newline at end of file
+.DS_Store
+.mypy_cache
\ No newline at end of file
diff --git a/condense/summarizer.py b/condense/summarizer.py
index bd6a61c5..c746e790 100644
--- a/condense/summarizer.py
+++ b/condense/summarizer.py
@@ -1,9 +1,8 @@
-import os
 import re
 import sys
 import logging
 import argparse
-from typing import List
+from typing import Dict, List, Tuple
 
 import nltk
 from transformers import pipeline
@@ -34,44 +33,68 @@ def make_parser() -> argparse.ArgumentParser:
     return parser
 
 
-def clean_data(data: List[dict]) -> str:
+def clean_data(data: List[Dict]) -> List[Dict]:
+    final_data = []
     if data:
         sentences = []
+        start, end, cnt = 0.00, 0, 0
         # Removing extra whitespaces
         for sentence in data:
+            end = sentence["end"]
+            cnt += 1
             sentence_text = re.sub(
                 "[^a-zA-Z0-9.,;:()'\"\\s]", "", sentence["text"]
             )  # Add special symbols to preserve inside the square brackets with numbers and characters
             sentence_sym = re.sub("\\s+", " ", sentence_text)
             sentences.append(sentence_sym.strip())
-        display = " ".join(sentences)
-        return display
+            # The model maximum tokens is 500, and each sentence is approximately 10 words, so to be on the safe side, we have considered cnt = 12, so maximum 10 sentences will be used
+            if cnt == 12:
+                text = " ".join(sentences)
+                final_data.append({"start": start, "end": end, "text": text})
+                start, cnt = end, 0
+                sentences = []
+
+        return final_data
     else:
         raise ValueError("No data found")
 
 
-def summerize_text(video_url: str) -> str:
-    nltk.download("punkt")
-    transcript_data = get_transcript(video_url)
-    data_clean = clean_data(transcript_data)
-    sentences = nltk.tokenize.sent_tokenize(data_clean)
-    data = " ".join(sentences)
-
+def get_summary(data: List[Dict[str, str]]) -> Tuple[List[Dict], List[Dict]]:
     summarizer = pipeline("summarization")
-    max_chunk_length = 400  # Define the maximum length for each chunk
-    chunks = [data[i : i + max_chunk_length] for i in range(0, len(data), max_chunk_length)]
     summary = []
-    for chunk in chunks:
-        summary_text = summarizer(chunk, max_length=50, min_length=10, do_sample=False)[0]["summary_text"]
-        summary.append(summary_text)
-    return " ".join(summary)
+    for chunk in data:
+        summary_text = summarizer(chunk["text"], max_length=50, min_length=1, do_sample=False)[0]["summary_text"]
+        summary.append({"start": chunk["start"], "end": chunk["end"], "summary_text": summary_text})
+
+    time_stamp = []
+    for chunk in summary:
+        summary_text = summarizer(chunk["summary_text"], max_length=13, min_length=1, do_sample=False)[0][
+            "summary_text"
+        ]
+        time_stamp.append({"start": chunk["start"], "end": chunk["end"], "summary_text": summary_text})
+
+    return (summary, time_stamp)
+
+
+def summerize_text(video_url: str) -> Tuple[List[Dict], List[Dict]]:
+    nltk.download("punkt")
+    data = get_transcript(video_url)
+    data = clean_data(data)
+    for sentence in data:
+        sentences = nltk.tokenize.sent_tokenize(sentence["text"])
+        sentence["text"] = " ".join(sentences)
+
+    summary, time_stamp = get_summary(data)
+    return (summary, time_stamp)
 
 
 def main(argv=None) -> int:
     parser = make_parser()
-    args = parser.parse_args(argv)
-    summary = summerize_text(args.video_url)
-    print(summary)
+    argv = parser.parse_args(argv)
+    summary, time_stamp = summerize_text(argv.video_url)
+    summary_text = " ".join([f"{chunk['summary_text']}" for chunk in summary])
+    summary_dict = {"summary": summary_text, "time_stamp": time_stamp}
+    print(summary_dict)
 
     return 0