diff --git a/utils_nlp/models/transformers/abstractive_summarization_bertsum.py b/utils_nlp/models/transformers/abstractive_summarization_bertsum.py
index 03beb3e24..0984f7c65 100644
--- a/utils_nlp/models/transformers/abstractive_summarization_bertsum.py
+++ b/utils_nlp/models/transformers/abstractive_summarization_bertsum.py
@@ -318,7 +318,7 @@ def preprocess(self, story_lines, summary_lines=None):
                 if len(line) <= 0:
                     continue
                 story_lines_token_ids.append(
-                    self.tokenizer.encode(line, max_length=self.max_src_len)
+                    self.tokenizer.encode(line, truncation=True, max_length=self.max_src_len)
                 )
             except:
                 print(line)
@@ -333,7 +333,7 @@ def preprocess(self, story_lines, summary_lines=None):
                     if len(line) <= 0:
                         continue
                     summary_lines_token_ids.append(
-                        self.tokenizer.encode(line, max_length=self.max_tgt_len)
+                        self.tokenizer.encode(line, truncation=True, max_length=self.max_tgt_len)
                     )
                 except:
                     print(line)
diff --git a/utils_nlp/models/transformers/bertsum/predictor.py b/utils_nlp/models/transformers/bertsum/predictor.py
index 20a30b895..81b6beb76 100644
--- a/utils_nlp/models/transformers/bertsum/predictor.py
+++ b/utils_nlp/models/transformers/bertsum/predictor.py
@@ -256,7 +256,7 @@ def _fast_translate_batch(self, src, segs, mask_src, max_length, min_length=0):
             topk_log_probs = topk_scores * length_penalty
 
             # Resolve beam origin and true word ids.
-            topk_beam_index = topk_ids.div(vocab_size)
+            topk_beam_index = topk_ids.true_divide(vocab_size)
             topk_ids = topk_ids.fmod(vocab_size)
 
             # Map beam_index to batch_index in the flat representation.
@@ -267,7 +267,7 @@ def _fast_translate_batch(self, src, segs, mask_src, max_length, min_length=0):
 
             # Append last prediction.
             alive_seq = torch.cat(
-                [alive_seq.index_select(0, select_indices), topk_ids.view(-1, 1)], -1
+                [alive_seq.index_select(0, select_indices.view(-1).long()), topk_ids.view(-1, 1)],  -1
             )
 
             is_finished = topk_ids.eq(self.end_token)
@@ -310,9 +310,9 @@ def _fast_translate_batch(self, src, segs, mask_src, max_length, min_length=0):
                 )
             # Reorder states.
             select_indices = batch_index.view(-1)
-            src_features = src_features.index_select(0, select_indices)
+            src_features = src_features.index_select(0, select_indices.view(-1).long())
             dec_states.map_batch_fn(
-                lambda state, dim: state.index_select(dim, select_indices)
+                lambda state, dim: state.index_select(dim, select_indices.view(-1).long())
             )
 
         empty_output = [len(results["predictions"][b]) <= 0 for b in batch_offset]