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How will the inference process work for this test sequence? I want you to think about it before you look at my thoughts below. As useful as this encoder-decoder architecture is, there are certain limitations that come with it. This may make it difficult for the neural network to cope with long sentences.
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The performance of a basic encoder-decoder deteriorates rapidly as the length of an input sentence increases. So how do we overcome this problem of long sequences? This is where the concept of attention mechanism comes into the picture. It aims to predict a word by looking at a few specific parts of the sequence only, rather than the entire sequence. It really is as awesome as it sounds!
How much attention do we need to pay to every word in the input sequence for generating a word at timestep t? So, instead of looking at all the words in the source sequence, we can increase the importance of specific parts of the source sequence that result in the target sequence. This is the basic idea behind the attention mechanism. There are 2 different classes of attention mechanism depending on the way the attended context vector is derived:. Here, the attention is placed on all the source positions.
In other words, all the hidden states of the encoder are considered for deriving the attended context vector:. Here, the attention is placed on only a few source positions. Only a few hidden states of the encoder are considered for deriving the attended context vector:. We will be using the Global Attention mechanism in this article.
Customer reviews can often be long and descriptive. Analyzing these reviews manually, as you can imagine, is really time-consuming.
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This is where the brilliance of Natural Language Processing can be applied to generate a summary for long reviews. We will be working on a really cool dataset. Our objective here is to generate a summary for the Amazon Fine Food reviews using the abstraction-based approach we learned about above. You can download the dataset from here.
Keras does not officially support attention layer. So, we can either implement our own attention layer or use a third-party implementation. We will go with the latter option for this article. You can download the attention layer from here and copy it in a different file called attention.
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This dataset consists of reviews of fine foods from Amazon. These reviews include product and user information, ratings, plain text review, and summary. It also includes reviews from all other Amazon categories. Feel free to use the entire dataset for training your model if your machine has that kind of computational power. Performing basic preprocessing steps is very important before we get to the model building part. Using messy and uncleaned text data is a potentially disastrous move.
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So in this step, we will drop all the unwanted symbols, characters, etc. Here is the dictionary that we will use for expanding the contractions:. We need to define two different functions for preprocessing the reviews and generating the summary since the preprocessing steps involved in text and summary differ slightly.
We will perform the below preprocessing tasks for our data:. Output :. Here, we will analyze the length of the reviews and the summary to get an overall idea about the distribution of length of the text. This will help us fix the maximum length of the sequence:. We can fix the maximum length of the reviews to 80 since that seems to be the majority review length. Similarly, we can set the maximum summary length to We are getting closer to the model building part. Before that, we need to split our dataset into a training and validation set. A tokenizer builds the vocabulary and converts a word sequence to an integer sequence.
Go ahead and build tokenizers for text and summary:. We are finally at the model building part. But before we do that, we need to familiarize ourselves with a few terms which are required prior to building the model. I am using sparse categorical cross-entropy as the loss function since it converts the integer sequence to a one-hot vector on the fly. This overcomes any memory issues. Remember the concept of early stopping?
It is used to stop training the neural network at the right time by monitoring a user-specified metric. Our model will stop training once the validation loss increases:. We can infer that there is a slight increase in the validation loss after epoch So, we will stop training the model after this epoch. We are defining a function below which is the implementation of the inference process which we covered in the above section :.
Let us define the functions to convert an integer sequence to a word sequence for summary as well as the reviews:. This is really cool stuff. Even though the actual summary and the summary generated by our model do not match in terms of words, both of them are conveying the same meaning. Our model is able to generate a legible summary based on the context present in the text.
As I mentioned at the start of the article, this is a math-heavy section so consider this as optional learning. I still highly recommend reading through this to truly grasp how attention mechanism works. There are different types of attention mechanisms depending on the type of score function used. Consider the source sequence to be [x 1 , x 2 , x 3 , x 4 ] and target sequence to be [y 1 , y 2 ].
I know this was a heavy dosage of math and theory but understanding this will now help you to grasp the underlying idea behind attention mechanism. This has spawned so many recent developments in NLP and now you are ready to make your own mark! Find the entire notebook here. And congratulations on building your first text summarization model using deep learning! We have seen how to build our own text summarizer using Seq2Seq modeling in Python. And make sure you experiment with the model we built here and share your results with the community! I have used the below code snippet for displaying the summaries and I have updated the same in the article.
Thanks for pointing it out.
Thanks for the great article. I am kinda confused how did you execute model in the end to generate those summaries. Thanks for the great article, It looks like source and target are not defined in the final snippet. During model training, all the target sequences must contain the end token. So, during prediction, we can stop the inference when the end token is predicted. But, here in your case, the model is predicting padding token.
So, just be sure that all the target sequences during training have end token. Please find the notebook here. Hey Aravind, I am having similar issue as others , keyerror:0 What do you mean by end token? And I used your code snippets FYI. Start and End are the special tokens which are appended to the summaries that signal the start and end of the sentence. Please go to the link over here to find the entire notebook.
Hello Arvind, First of all thank you so much for this wonderful article. Got it done.. Hai Aravind I got the output from u r notebook Thanks for this great code. Thank u very much Jaya. Hi Arvind, I want to use this trained model on a different model on a different data set in which there are no summaries. The data set contains only feed backs.
Can this be done? If yes then what changes should I make to the code. Hi Arvind, I want to use this trained model on a different data set in which there are no summaries. Thanks Arvind!
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