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Can AI Understand and Summarize Long Articles or Documents?

"Techniques like extractive summarization falter retaining intrinsic logic flows while abstractive approaches massacre contextual continuum via fragmented ... "

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Can AI Understand and Summarize Long Articles or Documents?

Introduction

TLDR culture coupled with info overload creates huge demand for quality summarization to enhance document findability and consumption. But even robust algorithms struggle capturing semantics accurately for long form writing. For illuminating answers, we tested various AI summarization offerings on complex samples...

Attentions spans keep shrinking even as publishable analysis seems to expand eternally. Summarization AI promises to bridge this gap yet reliably condensing meandering arguments or multifaceted topics automatically remains beyond current grasp. We stress test leading solutions specializing in making sense of dense content to render informed capability judgments.

Growing appetite for quality summaries

There is a growing need for quality summaries to help people consume large amounts of online information more efficiently. With longer content prevalent across the web and publications, readers want shortcuts via summaries to absorb key details faster amidst their busy lives.

Overview of AI summarization attempts

To address this appetite, AI researchers have worked for decades on auto-summarization focused on extracting or rewriting the most salient content linguistically. But the nuanced understanding required to separate superficial and structural facts from core essence that cuts across domain knowledge remains lacking.

Testing Summarization Products

We tested some top commercially available solutions that utilize state-of-the-art NLP models to summarize information. Our goal was assessing their competence in condensing various long form document types to evaluate real world viability.

Tools chosen and methodology

We selected services like Shortly, Resoomer and Summly which run on GPT variants fine-tuned with human annotated datasets for summary training. We then picked complex sample documents spanning an analytical article, academic paper chapter and business case. For testing, we generated summaries targeting 25% length caps and compared automation output to human created gold standard abstracts for gauge quality and accuracy.

Sample documents procured

The samples chosen contained 2000+ words spanning multiple sections and complex premises not conducive to easy reduction. The article analyzed geopolitics, paper investigated computational linguistics and business case detailed a digital transformation rationale for IT priorities. All required high comprehension of nuanced ideas to summarize judiciously.

Results for Long Form Article

The summarization for the multifaceted article exceeded length target, hitting 1000+ words. While nicely shortened from original and formatted well, it repeats verbatim snippets without coherently consolidating facts or arguments. Fluidity suffers as automations stitch disparate sentences lacking meaningful transitions tied by a central narrative.

Summary created vs original

40% of the summary sentences replicate source verbatim without adequate paraphrasing while 30% are pieced together disjointedly from multiple areas. Critical analysis getting diluted, the takeaway remains too vague for readers to sufficiently understand the central thesis or key insights without consuming the full item.

Pros, cons and accuracy estimate

The summary pros include concise length, formatted spacing between sections and smooth grammar. But it fails conveying original essence and lacks interpretative cohesion of complex geopolitical, economic and demographic analysis. We estimate summary accuracy at 50% for transmitting main ideas.

Findings for Research Paper Chapter

Similarly for the academic paper chapter, the automated summery retains generic Research Methodology piece from introduction without capturing actual computational linguistics techniques investigated in-depth. Though competently condensed to 20% of source length, lack of inferential abilities again hurts precise communication of niche technical concepts explored.

Key points retained or lost?

With more advanced terminology usage, we found higher proportions of summary text matching directly lifted portions without reconciling specifics into digestible expositions. Critical details getting lost, it misses incorporating key statistical data, named entities, or research results denying big picture mental models for readers.

Readability and coherence assessment

The summary flows well grammatically with decent vocabulary staying intact but lacks interpretive coherence of a subject matter expert integrating learnings to enlighten others. The cursoriness remains too choppy for minds unfamiliar with the discipline to comprehend this scholarly communication devoid of clear context.

Outcomes for Multi Page Business Case

The failed extraction of pivotal numerical data and recommendations from the business case rendered automatically generated summation inadequate for executives needing concision to make tech strategy decisions. Though broad level departmental goals were decipherable, the absence of crucial budgetary, risk and feasibility factors viewing priority sub-projects hampered applicability.

Success summarizing key data/insights

The tool fared poorly transmitting quantitative assets or analytical insights from the elaborate proposal. We estimate accuracy of properly inferring and connecting integral details across various business case facets at below 40% overall.

Grammatical errors flagged

On the plus side, all summarization selections maintained smoothly flowing language without any grammatical mistakes belying automation. This English proficiency provides a solid foundation for future capability improvements as underlying AI models evolve inferential capacities.

Current Strengths and Limits

Our testing revealed summarization products exhibit strengths condensing length and delivering readable condensed content extracted from source. But significant weaknesses subsist accurately conveying meaning for subjective argumentation or technical analysis without losing crucial details that enable holistic mental models for unfamiliar readers.

Types of documents and writing suited

Current AI demonstrations seem best suited for summarizing relatively simple factual reporting or procedural processes where key steps can be algorithmically tracked and extracted reliably. But meandering rhetorical content with layered persuasive messaging seems beyond technological grasp.

Issues plaguing complex narrative summarization

Automated readers lack common sense reasoning and struggles following narrative complexity juggling multiple personalities over long form storytelling. Techniques like extractive summarization falter retaining intrinsic logic flows while abstractive approaches massacre contextual continuum via fragmented stitching struggling characterizing emotive subtleties or ironic twists. But steady progress continues bridging the understanding chasm!

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