diff --git a/evaluation/evaluate_text_generation.py b/evaluation/evaluate_text_generation.py
index 55f164c2f..192a64fae 100644
--- a/evaluation/evaluate_text_generation.py
+++ b/evaluation/evaluate_text_generation.py
@@ -1,4 +1,5 @@
 import numpy as np
+import torch.cuda
 from datasets import load_dataset
 from sacrebleu import corpus_bleu
 from transformers import pipeline
@@ -11,31 +12,54 @@ def sacrebleu_score(hypotheses, references):
     return corpus_bleu(hypotheses, [references]).score
 
 
+def _process_data(dataset_name, split):
+    '''Function for extracting expected columns and create a dataset.'''
+    if dataset_name == "xsum":
+        hf_dataset = load_dataset(dataset_name, "3.0.0", split=split)
+        dataset = KeyValueDataset.from_huggingface(
+            hf_dataset, TaskType.TEXT_TO_TEXT_GENERATION, ["document", "summary"]
+        )
+        return dataset
+    elif dataset_name == "cnn_dailymail":
+        hf_dataset = load_dataset(dataset_name,"3.0.0", split=split)
+        dataset = KeyValueDataset.from_huggingface(
+            hf_dataset, TaskType.TEXT_TO_TEXT_GENERATION, ["article", "highlights"]
+        )
+        return dataset
+    elif dataset_name == "big_patent":
+        hf_dataset = load_dataset(dataset_name, split)
+        dataset = KeyValueDataset.from_huggingface(
+            hf_dataset, TaskType.TEXT_TO_TEXT_GENERATION, ["description", "abstract"]
+        )
+        return dataset
+    elif dataset_name == "billsum":
+        hf_dataset = load_dataset(dataset_name, split)
+        dataset = KeyValueDataset.from_huggingface(
+            hf_dataset, TaskType.TEXT_TO_TEXT_GENERATION, ["text", "summary"]
+        )
+
+
 def evaluate(
-    operation, evaluate_filter, model_name, dataset_name, split="test[:20%]"
-):
+    operation, evaluate_filter, model_name,
+        dataset_name, split="test[:20%]", is_cuda=torch.cuda.is_available()):
     # load model
-    if model_name is None:
-        model_name = "sshleifer/distilbart-xsum-12-6"
+    if model_name is None: model_name = "sshleifer/distilbart-xsum-12-6" # default model
     # load test set
-    if dataset_name is None:
-        dataset_name = "xsum"
+    if dataset_name is None: dataset_name = "xsum" # default dataset
 
     print(
         f"Loading <{dataset_name}> dataset to evaluate <{model_name}> model."
     )
-    hf_dataset = (
-        load_dataset(dataset_name, "3.0.0", split=split)
-        if dataset_name == "xsum"
-        else load_dataset(dataset_name, split=split)
-    )
-
-    dataset = KeyValueDataset.from_huggingface(
-        hf_dataset, TaskType.TEXT_TO_TEXT_GENERATION, ["document", "summary"]
-    )
     summarization_pipeline = pipeline(
-        "summarization", model=model_name, tokenizer=model_name
+        "summarization", model=model_name, tokenizer=model_name, device=0 if is_cuda else -1)
+    #percent = f"[{split.split('[')[-1]}" if "[" in split else ""
+    #if dataset_name == "wikihow": split = "all[:1%]"  # f"all{percent}"
+
+    dataset = _process_data(dataset_name, split)
+    print(
+        f"Here is the performance of the model {model_name} on the {split} split of the {dataset_name} dataset"
     )
+
     print(
         f"Here is the performance of the model {model_name} on the {split} split of the {dataset_name} dataset"
     )
@@ -55,20 +79,16 @@ def evaluate(
 
 
 def filter_performance(dataset, summarization_pipeline, filter):
+    '''Evaluate performance on filtered dataset.'''
     print("Here is the performance of the model on the filtered set")
     filtered_dataset = dataset.apply_filter(filter, subfields=["document"])
     return performance_on_dataset(filtered_dataset, summarization_pipeline)
 
 
-"""
-Evaluates performance on the original set
-and on the perturbed set.
-"""
-
-
 def transformation_performance(
     dataset, summarization_pipeline, transformation
 ):
+    '''Evaluates performance on the original set and on the perturbed set.'''
     performance = performance_on_dataset(
         dataset, summarization_pipeline
     )  # 15.989 BLEU
@@ -83,11 +103,13 @@ def transformation_performance(
 
 
 def performance_on_dataset(dataset, summarization_pipeline):
+    '''Evaluate performance on a given dataset.'''
     references = []
     raw_hypotheses = []
     print(f"Length of Evaluation dataset is {len(dataset)}")
 
-    for example in dataset:
+    for i,example in enumerate(dataset):
+        print(i)
         article, gold_summary = example
         max_len = (
             len(gold_summary.split(" ")) + 10
diff --git a/evaluation/leaderboard_wrapper.py b/evaluation/leaderboard_wrapper.py
index eeaa4def1..9a69d1562 100644
--- a/evaluation/leaderboard_wrapper.py
+++ b/evaluation/leaderboard_wrapper.py
@@ -26,6 +26,11 @@
         ("roberta-large-mnli", "multi_nli"),
         ("textattack/roberta-base-imdb", "imdb"),
     ],
+    "TEXT_TO_TEXT_GENERATION": [
+        ("mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization", "cnn_dailymail"),
+        ("google/pegasus-billsum", "billsum"),
+        ("google/bigbird-pegasus-large-bigpatent", "big_patent"),
+    ],
     "TEXT_TAGGING": [],
     "DIALOGUE_ACT_TO_TEXT": [],
     "TABLE_TO_TEXT": [],