Selecting the Appropriate AI Tool: Summarization

AI is a transformative force in many sectors, with a wide range of tools and technologies available. It’s important to select the right technology for the task at hand. AI adoption requires careful alignment of the chosen tool with task objectives to avoid underutilization, sub-optimal outcomes, and counterproductive results.

Designing a Pathway for Effective AI Tool Selection

The field of AI offers a broad range of tools capable of processing and analyzing different data types, including text, image, audio, and video. The selection of an AI tool is contingent upon a clear understanding of the task objective and the nature of the data at hand. This alignment ensures efficient utilization of AI capabilities and paves the way for successful outcomes.

  • Understanding task objectives: Defining the task objective involves identifying the problem to solve, understanding the desired outcome, and outlining the key performance indicators.
  • Recognizing data types: Different AI tools are designed to handle different data types. Text-based data is best handled by Natural Language Processing (NLP) tools, images by computer vision algorithms, audio data by speech recognition and processing tools, and video data often requires a combination of computer vision and audio processing algorithms.
AI Tools for Summarization

AI tools for summarization tasks are designed to condense lengthy, detailed information into a more manageable and succinct format without losing the essential points. From extracting the central ideas of complex research articles to presenting the main events of a long video, these tools employ advanced machine learning techniques to understand, interpret, and distill data across various formats and domains.


Text Summarization

Text summarization is a subfield of NLP that deals with creating shortened versions of text documents while preserving key information.

Application: Automated summarization, Abstractive and Extractive summarization, Contextual understanding, Customization, and Real-time summarization.

AI has made it possible to automatically generate summaries from long text content, making the process quicker and more scalable than traditional manual techniques. AI also facilitates both extractive and abstractive summarization. Extractive summarization involves identifying key phrases or sentences from the original text and combining them to form a summary. In contrast, abstractive summarization is about understanding the original text and creating new sentences to provide a condensed version, much like a human would do. AI has brought significant improvements in both types, especially abstractive summarization, which is a more complex task.

Furthermore, with techniques such as deep learning and transformers-based models like BERT, GPT, etc., AI can now generate summaries that better understand the context, semantics, and nuances of the original text. This results in more accurate and meaningful summaries. AI also allows the customization of summaries based on specific needs. For example, it can generate shorter or longer summaries based on user requirements or even focus on specific aspects of the text, such as summarizing only the results in a research paper. Additionally,  AI can provide real-time summarization of data streams like news feeds, social media updates, financial reports, etc., thereby aiding quick decision-making and trend spotting.

🛠️ Tools: Genei, Jasper, Pepper Content.


Image Summarization

Image summarization, also sometimes referred to as image compression, is the process of extracting the most important content or features from an image or a set of images. The goal is to provide a comprehensive and meaningful representation of the original image(s) that reduces redundancy and computational and storage requirements while retaining the crucial elements.

Application: Object detection and recognition, Contextual understanding, and Textual summaries.

Old image summarization technologies were manual or used simple features, whereas AI can accurately recognize objects in images. Advanced models understand the context, making connections between objects in the image and the environment. For example, they can determine if a person is indoors or outdoors based on the presence of certain objects in the image. AI also enables the creation of textual summaries for images. These summaries can provide context and explanation about what is happening in the image, thereby making them more accessible to people with visual impairments or for use in search engine optimization.

🛠️ Tools:

  • Image summarization: Google Cloud Vision API, Summly.
  • Contextual understanding: Clarifai, Imagga.
  • Textual summaries: Picasion, DeepAI, Flickr Vision API.


Audio Summarization

Audio summarization creates concise summaries of longer audio content. The goal is to provide a shorter version that contains the most important and relevant information from the original audio. For audio clips that contain speech, the audio is transcribed into text before applying text summarization techniques.

Application: Speech recognition, Speaker diarization.

The process of audio summarization begins with transcribing audio to text using Automatic Speech Recognition (ASR) technology, which converts spoken language into written words. Furthermore,  In multi-speaker audio files, it’s necessary to identify individual speakers and attribute speech to them correctly. This process is known as speaker diarization and helps in providing context to the summarized text.

🛠️ Tools: AudioRecap,,


Visual Summarization

Visual summarization is a process used to shorten a video or extract the most important and relevant parts of it. The aim is to provide a brief version of the video content that still conveys the core information or story.

Application: Static and Dynamic summarization.

Static (or keyframe) summarization involves extracting certain frames from the video to provide a representation of the content. The chosen frames, or keyframes, are typically those that best capture the events or actions within the video. This type of summarization results in a set of static images. On the other hand, dynamic (or skimming) summarization involves creating a shorter version of the original video, keeping the temporal aspect of the content. This form of summarization is more complex because it requires the selection and sequencing of specific scenes or segments to create a coherent and meaningful short video.

Sifting through vast amounts of video content can be overwhelming, but summarization tools can help researchers, journalists, investigators, and others quickly identify and focus on the most relevant content, saving significant time. Additionally, these tools also aid video editors and content creators in identifying key moments in raw footage, thus speeding up the editing and production process. In security operations, summarization helps highlight unusual or notable activities, improving response times.

Summarizing information is also ideal for creating condensed versions of lectures or training materials, making it easier for learners to review and retain information, leading to more efficient learning. Furthermore, companies like Netflix or YouTube could use video summarization to provide users with brief previews or “trailers” of content, thus helping them decide what to watch more quickly and enhancing their overall user experience.

🛠️ Tools:

  • Static summarization: Kapwing, Wibbitz, Vyond.
  • Dynamic summarization: Summly, TLDR, Vooks.