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July 10, 2023

AI-driven text summarization: Challenges and opportunities

Author:




Edwin Lisowski

CSO & Co-Founder


Reading time:




9 minutes


As organizations continue to create vast amounts of textual data, traditional, manual summarization methods are no longer feasible. To combat this challenge, many organizations are now turning to AI-driven text summarization tools to get an accurate summary of longer text documents, which ultimately helps them consume relevant information faster and discover new information.

This guide will explore AI text summarization in its entirety, including challenges faced by organizations in its implementation and the opportunities it provides.

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What is AI text summarization?

Text summarization is the process of creating short, accurate, and fluent summaries of longer text documents in a bid to address the challenges of leveraging the ever-growing amount of text data. [1]

AI-driven text summarization takes the summarization process a step further by employing ML and NLP tools to automatically break down lengthy text into easily digestible sentences and paragraphs. AI tools typically extract vital pieces of information from longer text documents without altering the meaning of the text.

The process offers several benefits, including:

  • A reduction in the time required to grasp lengthy pieces of text, such as long-form articles, without losing the meaning of the text.
  • Reduced prejudice compared to manual, human-operated summarization.
  • Improved efficacy in indexing

There are two main approaches to summarization. They include:

  • Extractive methods
  • Abstractive methods

Extractive text summarization

The extraction-based summarization technique involves pulling relevant key phrases from a text document and then combining them to make a summary. The extraction process is done in accordance with defined metrics that weigh the essential sections of sentences to create an aggregated summary without making any changes to the text.

Extractive text summarization

This results in a factual summary containing only the essential parts of the text, which enables easier digestion of information. However, depending on the NLP tools used, the summary may be grammatically ‘strange.’

Abstractive text summarization

Abstractive text summarization techniques employ advanced deep learning algorithms to paraphrase and shorten the original text document, much like how a human would. These algorithms typically focus on the most essential parts of the text document, thereby minimizing clutter and providing more value to the reader. [2]

abstractive text summarization

Additionally, the mere fact that abstractive text summarization generates new, coherent sentences and phrases representative of the original text means that the method effectively overcomes the grammatical inaccuracies presented by extractive text summarization methods.

It might be interesting for you: Text Extraction From Images Using Machine Learning

Opportunities for AI text summarization

Summarization has existed for quite a long time now. But, recently, the topic has attracted a lot of attention in the data science arena, mostly driven by advancements in natural language processing and deep learning technologies. This has led to the creation of various ML models and tools specifically engineered for that purpose.

Opportunities for AI text summarization

With that said, text summarization does not offer a one-size-fits-all solution when it comes to summarizing different text documents. For instance, the process and tools applied in summarizing simple text documents like articles and social media posts cannot effectively summarize a financial statement. Therefore, the tools and processes applied in summarization largely depend on the specific use case in question.

To that effect, organizations use different techniques and tools to effectively generate factual summaries. Here are some of the most notable opportunities presented by text summarization techniques across various industries.

Search marketing and SEO

Ever since search engines like Google shifted their focus to topical authority, it has become imperative for marketers to evaluate search queries for SEO purposes. To this effect, many online marketers are now relying on multi-document summarization tools to quickly analyze tens, or even hundreds of search results, thereby enabling them to skim important points and analyze shared themes. Ultimately, this helps them understand what their competitors are talking about, so they can gain a competitive advantage and make their pages rank higher.

Financial research

Financial institutions like investment banking firms spend a lot of resources to drive their decision-making and get viable metrics for their automated trading bots. This typically involves looking at and analyzing tons of financial reports and financial news. While some of this can be achieved with the help of analytics software, most of the decision-making is left to human beings, who are bound to hit a brick wall after a few hours of analyzing reports.

Fortunately, with specially-tailored AI-driven summarization tools, financial analysts can quickly skim through financial documents and derive market signals from the summarized content.

Media monitoring

The advent of social media and digital media broadcasting systems provided access to tons of information, much of which was hard to come by. Unfortunately, it also presented significant challenges in the form of information overload. Text summarization can help alleviate some of these issues by condensing the continuous streams of information into smaller, easily understandable pieces of information.

Video scripting

86% of marketing professionals use videos as their preferred marketing tools. [3] Although most of this is done through video platforms like social media and YouTube, some organizations also use professional platforms like LinkedIn to share long, research-intensive posts that may be difficult to understand with traditional scripting methods.

Automated summarization can help the target audience better understand the scripted content by providing easily skimmable scripts that incorporate research from numerous sources.

Patent research

Patent research is a tedious, time-consuming process. Regardless of whether you’re trying to file a new patent or doing marketing intelligence research, the sheer volume of text data you have to comb through can be overwhelming, to say the least. However, with automated summarization tools, you can be able to easily extract the most salient claims, thereby saving time and energy.

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Major challenges facing AI-driven text summarization

Major challenges facing AI-driven text summarization

Output quality

One of the biggest challenges facing the use of AI text summarization tools is controlling the quality of output. Evaluation tools like ROUGE and BLEU can help measure the overlap between generated summaries and referenced texts. However, these tools aren’t very effective in measuring the grammatical, semantical, and factual correctness of the summarized text.

Take abstractive text summarization, for instance. By paraphrasing the original content, abstractive models may produce summaries that contain words or numbers not present in the original text, which negatively impacts the quality and factuality of the summarized text. Similarly, extractive summarization may produce grammatically-incorrect summaries, leading to inconsistencies in the summarized text’s grammatical and semantic correctness.

Multi-document summarization

Multi-document summarization is a vital element of many analytics and research-intensive use cases like financial research. Unfortunately, it’s quite challenging to aggregate multiple documents into a single summary. The process is further complicated when the documents have different languages. In this case, the input files become so large that most abstract models, which use transformer-based models running on quadratic models, can’t handle it. [4]

Issues selecting important sentences

Which sentences are more important than others? While this might be easy for a human to decide, AI summarization tools may have a hard time deciding which sentences to include in the summary. In some cases, the model may prioritize irrelevant content in lieu of important elements in the referenced text, leading to less informative outputs. Therefore, developers need to employ standard benchmarks for effective consideration of the summary.

Model ineffectiveness in different scenarios

Most text summarization models are trained on specific types of texts, making them inefficient in summarizing text documents that don’t fit their initial training criteria. For instance, AI summarization tools trained on summarizing political documents may not work well with documents on sports. This means that multifaceted organizations may need different summarization tools for different purposes, thus increasing operational costs.

Model hallucination

Most modern text summarization models can generate coherent, grammatically-correct text. Unfortunately, they are also prone to generating content summaries not backed by the original text. Borrowed from psychology, this can be termed as model hallucination.

There are two types of model hallucination:

  • Intrinsic
  • Extrinsic

In intrinsic model hallucination, the generated summary contradicts the source document. Conversely, extrinsic model hallucination occurs when the generated summary cannot be verified from the source document. For instance, if the source document states that an earthquake happened in 2022, an intrinsic hallucination would say it happened in 2015, while an extrinsic hallucination would talk about an earthquake – something that was never mentioned in the source document.

In most cases, hallucinations occur in large language models. They typically introduce facts learned from their training data, which is vastly unrelated to the source document.

Challenges with long sentences

Scaling the length of your output summary significantly increases the chances of the model providing unfactual and grammatically-incorrect outputs. For instance, most models have an accuracy of up to 95% when dealing with short sentences. Their accuracy significantly drops to about 60% with longer sentences.

Computational complexity

Most machine learning algorithms and natural language processing models are designed to understand the relationship between every word in a sentence. These relationships increase significantly as you scale the number of words in the input document. Consequently, this increases the computational complexity of summarizing the given document, thus increasing the chances of the model missing some important information in the reference document.

Wrapping up

AI text summarization has come a long way from traditional human resource-intensive methods to AI-driven automatic summarization. The two common approaches to text summarization (extractive and abstractive) each present a unique set of challenges and benefits, making them uniquely suitable for different use cases.

However, these models face various issues arising from the accuracy, factuality, and grammatical correctness of their outputs. But, as developers leverage advancements in deep learning and natural language processing technologies, text summarization models may become more effective and efficient in the near future.

Ebook: AI Document Analysis in Business

References

[1] Sciencedirect.com. Text Summarization. URL: https://www.sciencedirect.com/topics/computer-science/text-summarization. Accessed July 6, 2023
[2] Ieeexplore.ieee.org. An approach to abstractive text summarization. URL: https://ieeexplore.ieee.org/document/7054161. Accessed July 6, 2023
[3] Amazonaws.com. Wyzowl Video Survey. URL: https://wyzowl.s3.eu-west-2.amazonaws.com/pdfs/Wyzowl-Video-Survey-2021.pdf . Accessed July 6, 2023
[4] Diva-portal.org. Evaluation of the Transformer Model for Abstractive Text Summarization. URL: https://www.diva-portal.org/smash/get/diva2:1368180/FULLTEXT01.pdf. Accessed July 6, 2023



Category:


Artificial Intelligence