Text Summarization using TF-IDF

Easy implementation using Python ft. Streamlit App

In the Article Text summarization in 5 steps using NLTK, we saw how we summarize the text using the Word Frequency Algorithm.

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Now, we’ll summarize the text using the Tf-IDF Algorithm.

Term Frequency * Inverse Document Frequency

In a simple language, TF-IDF can be defined as follows:

A High weight in TF-IDF is reached by a high term frequency(in the given document) and a low document frequency of the term in the whole collection of documents.

TF-IDF algorithm is made of 2 algorithms multiplied together.

Term Frequency

Term frequency (TF) is how often a word appears in a document, divided by how many words there are.

TF(t) = (Number of times term t appears in a document) / (Total number of terms in the document)

Inverse document frequency

Term frequency is how common a word is, and inverse document frequency (IDF) is how unique or rare a word is.

IDF(t) = log_e(Total number of documents / Number of documents with term t in it)

Example,
Consider a document containing 100 words wherein the word apple appears 5 times. The term frequency (i.e., TF) for apple is then (5 / 100) = 0.05.

Now, assume we have 10 million documents and the word apple appears in one thousand of them. Then, the inverse document frequency (i.e., IDF) is calculated as log(10,000,000 / 1,000) = 4.

Thus, the TF-IDF weight is the product of these quantities: 0.05 * 4 = 0.20.

Easy, right? We’ll use the same formula to generate the summary.

Oh Yeah, I Love Math.

The 9 steps implementation

Perquisites Python3, NLTK library of python, Your favourite text editor or IDE

1. Tokenize the sentences

We’ll tokenize the sentences here instead of words. And we’ll give weight to these sentences.

2. Create the Frequency matrix of the words in each sentence.

We calculate the frequency of words in each sentence.

The result would be something like this:

{'\nThose Who Are ': {'resili': 1, 'stay': 1, 'game': 1, 'longer': 1, '“': 1, 'mountain': 1}, 'However, I real': {'howev': 1, ',': 2, 'realis': 1, 'mani': 1, 'year': 1}, 'Have you experi': {'experienc': 1, 'thi': 1, 'befor': 1, '?': 1}, 'To be honest, I': {'honest': 1, ',': 1, '’': 1, 'answer': 1, '.': 1}, 'I can’t tell yo': {'’': 1, 'tell': 1, 'right': 1, 'cours': 1, 'action': 1, ';': 1, 'onli': 1, 'know': 1, '.': 1}...}

Here, each sentence is the key and the value is a dictionary of word frequency.

3. Calculate TermFrequency and generate a matrix

We’ll find the TermFrequency for each word in a paragraph.

Now, remember the definition of TF,

TF(t) = (Number of times term t appears in a document) / (Total number of terms in the document)

Here, the document is a paragraph, the term is a word in a paragraph.

Now the resultant matrix would look something like this:

{'\nThose Who Are ': {'resili': 0.03225806451612903, 'stay': 0.03225806451612903, 'game': 0.03225806451612903, 'longer': 0.03225806451612903, '“': 0.03225806451612903, 'mountain': 0.03225806451612903}, 'However, I real': {'howev': 0.07142857142857142, ',': 0.14285714285714285, 'realis': 0.07142857142857142, 'mani': 0.07142857142857142, 'year': 0.07142857142857142}, 'Have you experi': {'experienc': 0.25, 'thi': 0.25, 'befor': 0.25, '?': 0.25}, 'To be honest, I': {'honest': 0.2, ',': 0.2, '’': 0.2, 'answer': 0.2, '.': 0.2}, 'I can’t tell yo': {'’': 0.1111111111111111, 'tell': 0.1111111111111111, 'right': 0.1111111111111111, 'cours': 0.1111111111111111, 'action': 0.1111111111111111, ';': 0.1111111111111111, 'onli': 0.1111111111111111, 'know': 0.1111111111111111, '.': 0.1111111111111111}}

If we compare this table with the table we’ve generated in step 2, you will see the words having the same frequency have a similar TF score.

4. Creating a table for documents per words

This again a simple table which helps in calculating IDF matrix.

we calculate, “how many sentences contain a word”, Let’s call it the Documents per words matrix.

This is what we get now,

{'resili': 2, 'stay': 2, 'game': 3, 'longer': 2, '“': 5, 'mountain': 1, 'truth': 1, 'never': 2, 'climb': 1, 'vain': 1, ':': 8, 'either': 1, 'reach': 1, 'point': 2, 'higher': 1, 'today': 1, ',': 22, 'train': 1, 'power': 4, 'abl': 1, 'tomorrow.': 1, '”': 5, '—': 3, 'friedrich': 1, 'nietzsch': 1, 'challeng': 2, 'setback': 2, 'meant': 1, 'defeat': 3, 'promot': 1, '.': 45, 'howev': 2, 'realis': 2, 'mani': 3, 'year': 4, 'crush': 1, 'spirit': 1, 'easier': 1, 'give': 4, 'risk': 1}

i.e, the word resili appears in 2 sentences, power appears in 4 sentences.

5. Calculate IDF and generate a matrix

We’ll find the IDF for each word in a paragraph.

Now, remember the definition of IDF,

IDF(t) = log_e(Total number of documents / Number of documents with term t in it)

Here, the document is a paragraph, the term is a word in a paragraph.

Now the resultant matrix would look something like this:

{'\nThose Who Are ': {'resili': 1.414973347970818, 'stay': 1.414973347970818, 'game': 1.2388820889151366, 'longer': 1.414973347970818, '“': 1.0170333392987803, 'mountain': 1.7160033436347992}, 'However, I real': {'howev': 1.414973347970818, ',': 0.37358066281259295, 'realis': 1.414973347970818, 'mani': 1.2388820889151366, 'year': 1.1139433523068367}, 'Have you experi': {'experienc': 1.7160033436347992, 'thi': 1.1139433523068367, 'befor': 1.414973347970818, '?': 0.9378520932511555}, 'To be honest, I': {'honest': 1.7160033436347992, ',': 0.37358066281259295, '’': 0.5118833609788743, 'answer': 1.414973347970818, '.': 0.06279082985945544}, 'I can’t tell yo': {'’': 0.5118833609788743, 'tell': 1.414973347970818, 'right': 1.1139433523068367, 'cours': 1.7160033436347992, 'action': 1.2388820889151366, ';': 1.7160033436347992, 'onli': 1.2388820889151366, 'know': 1.0170333392987803, '.': 0.06279082985945544}}

Compare the same with the TF matrix and see the difference.

6. Calculate TF-IDF and generate a matrix

Now we have both the matrix and the next step is very easy.

TF-IDF algorithm is made of 2 algorithms multiplied together.

In simple terms, we are multiplying the values from both the matrix and generating new matrix.

{'\nThose Who Are ': {'resili': 0.04564430154744574, 'stay': 0.04564430154744574, 'game': 0.03996393835210118, 'longer': 0.04564430154744574, '“': 0.0328075270741542, 'mountain': 0.05535494656886449}, 'However, I real': {'howev': 0.10106952485505842, ',': 0.053368666116084706, 'realis': 0.10106952485505842, 'mani': 0.08849157777965261, 'year': 0.07956738230763119}, 'Have you experi': {'experienc': 0.4290008359086998, 'thi': 0.2784858380767092, 'befor': 0.3537433369927045, '?': 0.23446302331278887}, 'To be honest, I': {'honest': 0.34320066872695987, ',': 0.07471613256251859, '’': 0.10237667219577487, 'answer': 0.2829946695941636, '.': 0.01255816597189109}, 'I can’t tell yo': {'’': 0.0568759289976527, 'tell': 0.15721926088564644, 'right': 0.12377148358964851, 'cours': 0.19066703818164435, 'action': 0.13765356543501517, ';': 0.19066703818164435, 'onli': 0.13765356543501517, 'know': 0.11300370436653114, '.': 0.006976758873272827}}

7. Score the sentences

Scoring a sentence is differs with different algorithms. Here, we are using Tf-IDF score of words in a sentence to give weight to the paragraph.

This gives the table of sentences and their respected score:

{'\nThose Who Are ': 0.049494684794344025, 'However, I real': 0.09203831532832171, 'Have you experi': 0.3239232585727256, 'To be honest, I': 0.16316926181026162, 'I can’t tell yo': 0.12383203821623005}

8. Find the threshold

Similar to any summarization algorithm, there can be different ways to calculate a threshold value. We’re calculating the average sentence score.

We get the following score as an average:

0.15611302409372044

9. Generate the summary

Algorithm: Select a sentence for a summarization if the sentence score is more than the average score.

# Everything in one place: Algorithms Assemble 😆

For the threshold, we’ve used 1.3x of the average score. You can play with such variables to generate the summary as you like.

Test drive?

Original text:

Those Who Are Resilient Stay In The Game Longer “On the mountains of truth you can never climb in vain: either you will reach a point higher up today, or you will be training your powers so that you will be able to climb higher tomorrow.” — Friedrich Nietzsche Challenges and setbacks are not meant to defeat you, but promote you. However, I realise after many years of defeats, it can crush your spirit and it is easier to give up than risk further setbacks and disappointments. Have you experienced this before? To be honest, I don’t have the answers. I can’t tell you what the right course of action is; only you will know. However, it’s important not to be discouraged by failure when pursuing a goal or a dream, since failure itself means different things to different people. To a person with a Fixed Mindset failure is a blow to their self-esteem, yet to a person with a Growth Mindset, it’s an opportunity to improve and find new ways to overcome their obstacles. Same failure, yet different responses. Who is right and who is wrong? Neither. Each person has a different mindset that decides their outcome. Those who are resilient stay in the game longer and draw on their inner means to succeed.

I’ve coached many clients who gave up after many years toiling away at their respective goal or dream. It was at that point their biggest breakthrough came. Perhaps all those years of perseverance finally paid off. It was the 19th Century’s minister Henry Ward Beecher who once said: “One’s best success comes after their greatest disappointments.” No one knows what the future holds, so your only guide is whether you can endure repeated defeats and disappointments and still pursue your dream. Consider the advice from the American academic and psychologist Angela Duckworth who writes in Grit: The Power of Passion and Perseverance: “Many of us, it seems, quit what we start far too early and far too often. Even more than the effort a gritty person puts in on a single day, what matters is that they wake up the next day, and the next, ready to get on that treadmill and keep going.”

I know one thing for certain: don’t settle for less than what you’re capable of, but strive for something bigger. Some of you reading this might identify with this message because it resonates with you on a deeper level. For others, at the end of their tether the message might be nothing more than a trivial pep talk. What I wish to convey irrespective of where you are in your journey is: NEVER settle for less. If you settle for less, you will receive less than you deserve and convince yourself you are justified to receive it.

“Two people on a precipice over Yosemite Valley” by Nathan Shipps on Unsplash Develop A Powerful Vision Of What You Want “Your problem is to bridge the gap which exists between where you are now and the goal you intend to reach.” — Earl Nightingale I recall a passage my father often used growing up in 1990s: “Don’t tell me your problems unless you’ve spent weeks trying to solve them yourself.” That advice has echoed in my mind for decades and became my motivator. Don’t leave it to other people or outside circumstances to motivate you because you will be let down every time. It must come from within you. Gnaw away at your problems until you solve them or find a solution. Problems are not stop signs, they are advising you that more work is required to overcome them. Most times, problems help you gain a skill or develop the resources to succeed later. So embrace your challenges and develop the grit to push past them instead of retreat in resignation. Where are you settling in your life right now? Could you be you playing for bigger stakes than you are? Are you willing to play bigger even if it means repeated failures and setbacks? You should ask yourself these questions to decide whether you’re willing to put yourself on the line or settle for less. And that’s fine if you’re content to receive less, as long as you’re not regretful later.

If you have not achieved the success you deserve and are considering giving up, will you regret it in a few years or decades from now? Only you can answer that, but you should carve out time to discover your motivation for pursuing your goals. It’s a fact, if you don’t know what you want you’ll get what life hands you and it may not be in your best interest, affirms author Larry Weidel: “Winners know that if you don’t figure out what you want, you’ll get whatever life hands you.” The key is to develop a powerful vision of what you want and hold that image in your mind. Nurture it daily and give it life by taking purposeful action towards it.

Vision + desire + dedication + patience + daily action leads to astonishing success. Are you willing to commit to this way of life or jump ship at the first sign of failure? I’m amused when I read questions written by millennials on Quora who ask how they can become rich and famous or the next Elon Musk. Success is a fickle and long game with highs and lows. Similarly, there are no assurances even if you’re an overnight sensation, to sustain it for long, particularly if you don’t have the mental and emotional means to endure it. This means you must rely on the one true constant in your favour: your personal development. The more you grow, the more you gain in terms of financial resources, status, success — simple. If you leave it to outside conditions to dictate your circumstances, you are rolling the dice on your future.

So become intentional on what you want out of life. Commit to it. Nurture your dreams. Focus on your development and if you want to give up, know what’s involved before you take the plunge. Because I assure you, someone out there right now is working harder than you, reading more books, sleeping less and sacrificing all they have to realise their dreams and it may contest with yours. Don’t leave your dreams to chance.

Summarized text:

Have you experienced this before? Who is right and who is wrong? Neither. It was at that point their biggest breakthrough came. Perhaps all those years of perseverance finally paid off. It must come from within you. Where are you settling in your life right now? Could you be you playing for bigger stakes than you are? So become intentional on what you want out of life. Commit to it. Nurture your dreams.

Voila! You’ve just summarized the text using the infamous Tf-IDF algorithm. Show off to your friends now. 😎

What next?

1. Play with it: Try to change the threshold value (1.5x to 1.3x or 1.8x) and see what comes out.

2. Extend it: You can also extend it to summarize a text using “a number of lines/sentence you want”.

Note: This is an extractive text summarization technique.

Find the full code here

akashp1712/nlp-akash