Understanding Content Summarization with 3RDi Search
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We all have experienced how a short summary can make it easy for us to understand what to expect in a book or a comprehensive write-up. After all, it saves the reader's time and he or she can quickly understand what a large volume of content is all about. In today's times, data is fast expanding and emerging as one of the key driving forces behind the success of enterprises. Now is the time to consider ways to make the best use of this data and content summarization is a one of the key elements of data analysis.
The new age enterprise search platform, 3RDi Search being an example, is equipped with the right set of data mining capabilities to help enterprises use enterprise data to their advantage. Content summarization is one of the many capabilities of a platform like 3RDi Search that uses NLP and Artificial Intelligence to analyze the most complex unstructured data. It is nothing but a technique that involves creation of a summary of large volumes of text, retaining the key facts as well as the overall meaning. Fascinating, isn't it? So, how does it work?
Content summarization is no magic after all, and it is all about training the algorithms to identify sections in the content that contain the most relevant information and then extracting the information before presenting it to the user. While this may sound like a pretty simple process, executing it will involve enormous amount of time and costs if done manually. The algorithm of the advanced enterprise search platform does this is easily.
The Need for Automated Content Summarization
As mentioned above, content summarization is an extremely time consuming task that is next impossible to manage manually. As the volumes of data in enterprises continue to rise, the need for an automated platform to make sense of this data, is the need of the hour. What's more, 3RDi Search also provides maximum search relevancy, which means it has the capability to retrieve the most minute details about a specific topic that is hidden deep within the enterprise data. The non-relevant data is left behind.
The use cases for content summarization can be many including the following:
Key Steps of Content Summarization
When we look at the content summarization capabilities of an advanced enterprise search platform like 3RDi Search, the steps involved are as follows:
Step 1: Extracting sentences from paragraphs. This involves converting a paragraph into a series of individual sentences.
Step 2: Text Processing which involves removal of "stop words" and redundant and common words (and, the, this, etc.) that do not add to the meaning of the sentences.
For example, consider the two sentences below
Elisa went to the city by train and boarded a bus to the village. (Original sentence)
Elisa went city by train boarded bus to village (modified sentence.)
Step 3: The third step is referred to as tokenization and involves the list of all the words present in all the sentences of the paragraph (excluding the words eliminated in Step 2.)
Step 4: Evaluating the weighted ordered frequency of the words. This is calculated by dividing the number of instances a certain word appears in the paragraph by the total number of instances of the word that occurs most frequently in the paragraph. This gives a decimal value.
Step 5: Replacing all the words with their respective ordered frequencies and adding its values for the sentences. The sentence with the highest value is considered most relevant, followed by the sentence with the second highest value and so on. Highest value sentences are preferred as the summary of the content.
That was about the fundamentals of the content summarization capabilities of an advanced new age enterprise search platform like 3RDi Search.
The new age enterprise search platform, 3RDi Search being an example, is equipped with the right set of data mining capabilities to help enterprises use enterprise data to their advantage. Content summarization is one of the many capabilities of a platform like 3RDi Search that uses NLP and Artificial Intelligence to analyze the most complex unstructured data. It is nothing but a technique that involves creation of a summary of large volumes of text, retaining the key facts as well as the overall meaning. Fascinating, isn't it? So, how does it work?
Content summarization is no magic after all, and it is all about training the algorithms to identify sections in the content that contain the most relevant information and then extracting the information before presenting it to the user. While this may sound like a pretty simple process, executing it will involve enormous amount of time and costs if done manually. The algorithm of the advanced enterprise search platform does this is easily.
The Need for Automated Content Summarization
As mentioned above, content summarization is an extremely time consuming task that is next impossible to manage manually. As the volumes of data in enterprises continue to rise, the need for an automated platform to make sense of this data, is the need of the hour. What's more, 3RDi Search also provides maximum search relevancy, which means it has the capability to retrieve the most minute details about a specific topic that is hidden deep within the enterprise data. The non-relevant data is left behind.
The use cases for content summarization can be many including the following:
- Quick understanding of the theme or subject of a chunk of unstructured data
- Understanding what information is hidden deep within unstructured enterprise data
- Classification of unstructured data
- Fetching the most relevant search results
- Enhanced readability of documents
- Less time required to find information
- Answering questions precisely
- Reducing the size of the document
Key Steps of Content Summarization
When we look at the content summarization capabilities of an advanced enterprise search platform like 3RDi Search, the steps involved are as follows:
Step 1: Extracting sentences from paragraphs. This involves converting a paragraph into a series of individual sentences.
Step 2: Text Processing which involves removal of "stop words" and redundant and common words (and, the, this, etc.) that do not add to the meaning of the sentences.
For example, consider the two sentences below
Elisa went to the city by train and boarded a bus to the village. (Original sentence)
Elisa went city by train boarded bus to village (modified sentence.)
Step 3: The third step is referred to as tokenization and involves the list of all the words present in all the sentences of the paragraph (excluding the words eliminated in Step 2.)
Step 4: Evaluating the weighted ordered frequency of the words. This is calculated by dividing the number of instances a certain word appears in the paragraph by the total number of instances of the word that occurs most frequently in the paragraph. This gives a decimal value.
Step 5: Replacing all the words with their respective ordered frequencies and adding its values for the sentences. The sentence with the highest value is considered most relevant, followed by the sentence with the second highest value and so on. Highest value sentences are preferred as the summary of the content.
That was about the fundamentals of the content summarization capabilities of an advanced new age enterprise search platform like 3RDi Search.
Want to explore more about how an advanced enterprise platform like 3RDi Search can help your enterprise make the best possible use enterprise data? Visit www.3rdisearch.com or drop us an email on info@3rdisearch.com.
Want to know more about the text analysis capabilities of 3RDi Search? Read Explore the Capabilities of an Intelligent Search Platform
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