Plain Language Summarization
- PLS is a method that transforms complex, technical documents into concise, plain language summaries through abstraction and simplification.
- It leverages techniques such as unsupervised extractive methods and style transfer to achieve high compression and improved readability.
- Key metrics like n-gram novelty, compression ratio, and readability gains demonstrate its effectiveness while highlighting limitations of traditional extractive approaches.
Plain Language Summarization (PLS) is the task of condensing complex, often highly specialized documents—such as legal agreements, biomedical research, technical manuals, or other domain-specific texts—into summaries that are accessible, comprehensible, and actionable for non-expert audiences. The content must not only be condensed but also transformed in style and vocabulary, typically requiring significant abstraction, compression, and simplification beyond traditional summarization paradigms.
1. Task Definition and Motivation
PLS aims to address the accessibility gap in domains where technical documents are crucial for end-user decision-making but remain unreadable to the general public due to length, jargon, and structural complexity. For instance, the motivation for the task was articulated in "Plain English Summarization of Contracts" (Manor et al., 2019), which highlighted that unilateral contracts (e.g., terms of service) are pivotal in digital life yet rarely read or understood by users. PLS was proposed to enable users to more fully comprehend the terms they tacitly accept.
Distinct from standard summarization, which often optimizes for informativeness or brevity alone, PLS requires actively reshaping the linguistic form, compressing content more aggressively, and overcoming significant stylistic shifts. The output is assessed not only for fidelity and coverage but also for readability and accessibility by non-expert audiences.
2. Dataset Construction and Characteristics
A foundational challenge in PLS is curating and verifying aligned datasets that reflect the complex transformation between technical content and plain language. "Plain English Summarization of Contracts" (Manor et al., 2019) constructed an initial dataset comprising 446 section–summary pairs from online sources such as TL;DRLegal and TOS;DR, focusing on software licenses and privacy policies. Extraction involved careful manual curation, rejecting summaries that were simply repeated text, quotations, or opinions, and instead emphasizing those with true abstraction and simplification.
Lexical analyses revealed that PLS data is much more abstractive than traditional summarization benchmarks: over 40% of unigrams and up to 92% of 4-grams in summaries did not appear in the original legal text, in contrast to news-oriented datasets. Compression was similarly substantial—the mean summary length was approximately 17 words versus 105 words in source sections, yielding a compression rate of 0.31.
Readability metrics (Flesch-Kincaid, Coleman-Liau, SMOG, ARI) consistently indicated that plain language summaries are approximately six years simpler (by grade level) than the original legalese. Word association analyses using log odds ratios further identified that lexical simplification is essential, with more accessible terms dominating PLS outputs.
| Dataset | Ngram Novelty (4-grams) | Mean Summary Length | Compression Ratio | Readability Gain (years) |
|---|---|---|---|---|
| PLS Legal Dataset | 92% | ~17 | 0.31 | ~6 |
| DUC 2002 (News) | Lower | -- | -- | <1 |
3. Approaches and Baseline Methods
Given the scarcity of aligned training data in specialized domains, initial research emphasized unsupervised, extractive summarization methods. The contract PLS paper (Manor et al., 2019) evaluated TextRank (graph-based, PageRank-like), KLSum (greedy KL-divergence minimization), Lead-1/Lead-K (first sentence(s)), and Random-K (random sentence selection). Standard preprocessing included lowercasing, lemmatization, and stop word removal.
However, these extractive models performed poorly on legal PLS datasets, as measured by ROUGE-1/2/L, relative to performance on news summarization (e.g., DUC 2002). The low performance is attributable to the high degree of abstraction and the lack of overlap between salient plain language and source sentences. Even simple baselines such as Random-K neared the best extractive results, highlighting the difficulty of sentence extraction when source documents lack explicit topical structure or an "inverted pyramid" organization.
| Method | DUC 2002 ROUGE-L | Legal PLS ROUGE-L | Relative Effectiveness |
|---|---|---|---|
| Lead-K | High | Low | Reduced |
| TextRank | High | Low | Reduced |
| KLSum | High | Low | Reduced |
| Random-K | Lower | Close to best | Comparable |
4. Abstraction, Compression, and Readability Control
PLS outputs are characterized by high abstraction and structural transformation requirements. The degree of abstraction is measurable via n-gram novelty statistics and compression indices. Additionally, readability quantification leverages formulas such as:
- Flesch-Kincaid (F-K)
- Coleman–Liau (CL)
- SMOG
- Automated Readability Index (ARI)
The shift in lexical content can be formally analyzed using the log odds ratio:
where and denote the summary and source distributions, respectively. In the referenced paper, comparing top 50 summary-associated and original text-associated words with ARI and F–K formulas reveals grade level differences exceeding five years, underscoring one of the core challenges in PLS: style and accessibility transfer.
5. Limitations of Extractive Methods and Domain-specific Barriers
Baseline results consistently show that unsupervised extractive schemes cannot capture the abstraction, stylistic transformation, or compression intrinsic to effective PLS, particularly for legal or domain-specialized texts. Unlike news or scientific articles, which may benefit from extractive heuristics due to consistent topical structure, legal documents require innovations in style transfer and lexical simplification.
Supervised methods relying on finely aligned sentence pairs are impractical in the legal domain due to limited parallel corpora, further complicating the application of neural abstractive models. There is also a significant domain gap: existing text simplification or style transfer models are typically trained on general corpora (news, Wikipedia), not legal or policymaking documents.
6. Prospective Directions and Research Challenges
The paper calls for resource and technique development centered on:
- Unsupervised or weakly supervised lexical and sentence-level simplification (e.g., with distributed word representations)
- Rich semantic structure exploitation for abstraction
- Unsupervised style transfer approaches
- Expansion of annotated datasets and resource creation for legal or highly technical domains
Addressing PLS demands methods that achieve strong content selection, semantic approximation, syntactic simplification, and stylistic transformation—often in resource-scarce settings. The surveyed work establishes foundational insights and annotated datasets, laying groundwork for future research in legal domain simplification and broader PLS pipelines.
7. Conclusion and Significance
"Plain English Summarization of Contracts" (Manor et al., 2019) presents the first systematically annotated dataset for contract PLS, benchmarks traditional extractive methods, and identifies core challenges in abstraction, compression, and readability. The findings demonstrate that state-of-the-art unsupervised extractive methods are insufficient for generating accessible summaries of complex legal text. Effective PLS requires new models that blend simplification, style transfer, and sophisticated content planning, tailored to the domain-specific challenges of technical and legal language.
This research delineates the limitations of current extractive summarization in legal settings and establishes both the empirical and methodological baseline for future PLS resource and system development.