--- a
+++ b/extractiveSummarization/generate_summaries.py
@@ -0,0 +1,91 @@
+import sys, os
+import glob
+import itertools
+import shutil
+import time
+from pathlib import Path
+from nltk import sent_tokenize
+from summarizers import Lexrank
+
+import argparse
+parser = argparse.ArgumentParser()
+
+parser.add_argument('--train', action='store', metavar='path', type=str, required=True,  help='Directory path containing training documents. If test path not specified, also treated as testing documents.')
+parser.add_argument('--saveto', action='store', metavar='path', type=str, required=True, help='directory path for saving summaries produced')
+parser.add_argument('--test', action='store', metavar='path', type=str, help='directory path containing testing documents')
+parser.add_argument('--ntrain', action='store', type=int, metavar='n', help='First n number of documents to train on')
+parser.add_argument('--ntest', action='store', type=int, metavar='n', help='First n number of documents to produce summaries for')
+parser.add_argument('--threshold', action='store', type=float, help="default 0.03")
+parser.add_argument('--size', action='store', type=int, help='summary size. default 1')
+
+args = parser.parse_args()
+
+train_dir_path = args.train
+test_dir_path = args.test
+saveto_dir_path = args.saveto
+threshold = args.threshold or 0.03
+summary_size = args.size or 1
+
+if not os.path.isdir(train_dir_path):
+    print('The train path specified does not exist')
+    sys.exit()
+
+if test_dir_path and not os.path.isdir(test_dir_path):
+    print('The test path specified does not exist')
+    sys.exit()
+
+if not os.path.isdir(saveto_dir_path):
+    print('The save to path specified does not exist')
+    sys.exit()
+
+if args.ntest and args.ntest < 1:
+    print('ntest should be greater than 0')
+
+if args.ntrain and args.ntrain < 1:
+    print('ntrain should be greater than 0')
+
+start = time.time()
+
+train_documents = {}
+test_documents = {}
+
+for i, filepath in enumerate(glob.glob(os.path.join(train_dir_path, '*'))):
+    with open(filepath) as fp:
+        fname = Path(filepath).stem
+        sentences = [] 
+        for line in fp.readlines():
+            sentences.extend(sent_tokenize(line))
+        train_documents[fname] = sentences
+        if args.ntrain and i == args.ntrain - 1:
+            break
+
+if test_dir_path:
+    for i, filepath in enumerate(glob.glob(os.path.join(test_dir_path, '*'))):
+        with open(filepath) as fp:
+            fname = Path(filepath).stem
+            sentences = [] 
+            for line in fp.readlines():
+                sentences.extend(sent_tokenize(line))
+            test_documents[fname] = sentences
+        if args.ntest and i == args.ntest - 1:
+            break
+
+lxr = Lexrank(train_documents.values(), threshold=threshold)
+
+if test_dir_path:
+    documents = test_documents
+else:
+    documents = train_documents
+
+for i, fname in enumerate(documents):
+    summary = lxr.get_summary(documents[fname], summary_size=summary_size)#, threshold=.1)
+    joined_summary = " ".join(summary)
+    summary_path = os.path.join(saveto_dir_path, fname + ".sum")
+    with open(summary_path, 'w') as sum:
+        sum.write(joined_summary)
+    if args.ntest and i == args.ntest - 1:
+        break
+
+end = time.time()
+#print("----Summary----")
+print("Runtime " + str(end - start))