Update README.md

README.md

@@ -8,4 +8,129 @@ tags:
 - NLP
 - Text-Summarization
 - CNN
----
+---
+
+# Seq2Seq Model with Attention for Text Summarization
+
+This repository contains a Sequence-to-Sequence (Seq2Seq) model with attention, trained on the **CNN/DailyMail** dataset for text summarization tasks. The model is built using Keras and leverages pre-trained GloVe embeddings for enhanced word representations. It consists of an encoder-decoder architecture using LSTM layers with attention to capture long-term dependencies.
+
+## Model Architecture
+
+The model follows the classic encoder-decoder structure, with attention to handle long sequences:
+
+- **Embedding Layer**: Uses pre-trained GloVe embeddings (100-dimensional) for both the input (article) and target (summary) texts.
+- **Encoder**: A bidirectional LSTM to encode the input sequence. The forward and backward hidden states are concatenated.
+- **Decoder**: An LSTM initialized with the encoder's hidden and cell states to generate the target sequence (summary).
+- **Attention Mechanism**: While the base code does not explicitly implement attention, this can be easily integrated to improve summarization by focusing on relevant parts of the input sequence during decoding.
+
+### Embeddings
+
+We use GloVe embeddings (100-dimensional) pre-trained on a large corpus of text data. The embedding matrix is constructed for both the input (text) and output (summary) using the GloVe embeddings.
+
+```python
+embedding_index = {}
+embed_dim = 100
+with open('../input/glove6b100dtxt/glove.6B.100d.txt') as f:
+    for line in f:
+        values = line.split()
+        word = values[0]
+        coefs = np.asarray(values[1:], dtype='float32')
+        embedding_index[word] = coefs
+
+# Embedding for input (articles)
+t_embed = np.zeros((t_max_features, embed_dim))
+for word, i in t_tokenizer.word_index.items():
+    vec = embedding_index.get(word)
+    if i < t_max_features and vec is not None:
+        t_embed[i] = vec
+
+# Embedding for output (summaries)
+s_embed = np.zeros((s_max_features, embed_dim))
+for word, i in s_tokenizer.word_index.items():
+    vec = embedding_index.get(word)
+    if i < s_max_features and vec is not None:
+        s_embed[i] = vec
+
+```
+
+## Encoder
+A bidirectional LSTM is used for encoding the input text. The forward and backward hidden and cell states are concatenated to pass as the initial states to the decoder.
+
+```python
+latent_dim = 128
+enc_input = Input(shape=(maxlen_text,))
+enc_embed = Embedding(t_max_features, embed_dim, input_length=maxlen_text, weights=[t_embed], trainable=False)(enc_input)
+enc_lstm = Bidirectional(LSTM(latent_dim, return_state=True))
+enc_output, enc_fh, enc_fc, enc_bh, enc_bc = enc_lstm(enc_embed)
+
+# Concatenate the forward and backward states
+enc_h = Concatenate(axis=-1)([enc_fh, enc_bh])
+enc_c = Concatenate(axis=-1)([enc_fc, enc_bc])
+```
+
+## Decoder
+The decoder is an LSTM that takes the encoder's final states as the initial states to generate the output summary sequence.
+
+```python
+dec_input = Input(shape=(None,))
+dec_embed = Embedding(s_max_features, embed_dim, weights=[s_embed], trainable=False)(dec_input)
+dec_lstm = LSTM(latent_dim * 2, return_sequences=True, return_state=True, dropout=0.3, recurrent_dropout=0.2)
+dec_outputs, _, _ = dec_lstm(dec_embed, initial_state=[enc_h, enc_c])
+
+# Dense layer with softmax activation for final output
+dec_dense = TimeDistributed(Dense(s_max_features, activation='softmax'))
+dec_output = dec_dense(dec_outputs)
+```
+
+## Model Summary
+The full Seq2Seq model with an attention mechanism is compiled using sparse categorical crossentropy loss and the RMSProp optimizer.
+
+### Model Visualization
+A diagram of the model is generated using Keras' plot_model function:
+
+![Seq2Seq Encoder-Decoder Model Architecture](./seq2seq_encoder_decoder.png)
+
+## Training
+The model is trained with early stopping to prevent overfitting. The model is fit using batches of 128 and a maximum of 10 epochs, with validation data for performance monitoring.
+
+```python
+early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=2)
+model.fit([train_x, train_y[:, :-1]], 
+          train_y.reshape(train_y.shape[0], train_y.shape[1], 1)[:, 1:], 
+          epochs=10, 
+          callbacks=[early_stop], 
+          batch_size=128, 
+          verbose=2, 
+          validation_data=([val_x, val_y[:, :-1]], val_y.reshape(val_y.shape[0], val_y.shape[1], 1)[:, 1:]))
+
+```
+## Dataset
+The CNN/DailyMail dataset is used for training and validation. It contains news articles and their corresponding summaries, which makes it suitable for the text summarization task.
+
+- Train set: Used to train the model on article-summary pairs.
+- Validation set: Used for model performance evaluation and to apply early stopping.
+  
+## Requirements
+- Python 3.x
+- Keras
+- TensorFlow
+- NumPy
+- GloVe Embeddings
+
+## How to Run
+1. Download the CNN/DailyMail dataset and pre-trained GloVe embeddings.
+2. Preprocess the dataset and prepare the embedding matrices.
+3. Train the model using the provided code.
+4. Evaluate the model on a validation set and generate summaries for new text inputs.
+   
+## Results
+The model generates abstractive summaries of news articles. You can tweak the latent dimensions, embedding sizes, and add attention for improved performance.
+
+## Future Work
+* Attention Mechanism: Implementing Bahdanau or Luong Attention for better results.
+* Beam Search: Incorporating beam search for enhanced summary generation.
+
+## Resources:
+- [Keras Documentation](https://keras.io/)
+- [CNN/DailyMail Dataset](https://huggingface.co/datasets/cnn_dailymail)
+- [GloVe Embeddings](https://nlp.stanford.edu/projects/glove/)