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Any time someone talks about data, they never fail to mention that the amount of data produced every day is growing at an exponential rate. With so much content being produced on a daily basis, it is becoming humanly impossible to keep up to date. This is an especially important challenge for SEO specialists, who must constantly stay current to write relevant content for their client’s website.

Keeping website content up to date on the most recent trends is very important for an SEO specialist. If a website wants to place first in the relevant search rankings, it needs to ensure that it employs all the words that are of interest to their clients. As a result, an SEO specialist must stay up to date on all of the recent topics in their industry and to update the website content accordingly. Yet, no one has enough time to keep up with all of the articles and websites that focus on their industry. That is where text summaries can help. There are many different approaches to text summarization, but they all fall in one of two categories: extractive or abstractive.

Extractive summaries are generally easier to program. The idea behind extractive summary algorithms is that the sentences with the highest density of frequent words are the most important ones. If you think about it, it’s quite logical: the words that come up more frequently in an article are the words that are most closely related to the main topic. Since extractive summaries simply cherry pick a couple sentences, they are reliable as they will never change the meaning of a sentence. They do have several limitations, however. Because various sentences are simply glued together, the summary’s transitions and logical flow are likely to be very choppy. Moreover, extractive summary algorithms have difficulties summarizing longer texts. When cherry picking a handful of sentences from longer texts, such as books, it is unlikely that there are sentences that are very relevant to the entire text as a whole. As a result, the summaries will include very specific details from the book without synthesizing the main point. Abstractive summaries, on the other hand, are specifically meant to take into account these limitations.

Abstractive summaries are more complex than extractive ones. Abstractive summaries are expected to, in a way, understand what the article is talking about and to create sentences that focus on the big picture. The main algorithms used to create abstractive summaries use Reccurent Neural Networks (and more specifically LSTM). This type of algorithm is rather complex to train though and often requires a lot of data. Not only that but, since it tries to combine multiple sentences, the algorithm may sometimes mix up the actual meaning of the text. As a result, these types of algorithms are not always 100% reliable.

Just like all things in life, no approach is perfect. To choose the best methodology, you need to consider than main objectives of the project and weigh the pros and cons accordingly.

In any case, there is significant incentive to write effective text summarization algorithms as text summarization can prove to be very beneficial for multiple use cases. Besides helping SEO specialists keep up to date, text summarization can be very useful for meta data. Instead of having to create page summaries for search engines, an SEO specialist can run the webpage through the algorithm and simply print the meta description of the webpage. This practice is even more beneficial as it serves as a content check. SEO specialists can double check if the summary is consistent with the main point of the webpage or not. If the algorithm did not pick up on the right topics, it is an indication that the webpage is not conveying the intended message and that it may need to be refocused.

Have you ever used a text summarization technique? What were some of those projects?