Extracting Aspects Hierarchies using Rhetorical Structure Theory

{\L}ukasz Augustyniak; Przemys{\l}aw Kazienko; Tomasz Kajdanowicz

REVIEW 4 major objections 5 minor 24 references

Extracting Aspects Hierarchies using Rhetorical Structure Theory

T0 review · 4 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read The paper claims that unsupervised extraction of aspect hierarchies from product reviews, using rhetorical structure theory, reproduces human-coded ConceptNet relations with about 80% coverage on the top 50 aspect pairs.

desk verdict The RST-based aspect hierarchy idea is plausible, but the paper's headline 80% ConceptNet coverage is not yet evidence because the support set is broad and there is no baseline. read the letter →

arxiv 1909.01800 v1 pith:7XGWRFLV submitted 2019-09-04 cs.CL

{L}ukasz Augustyniak , Tomasz Kajdanowicz , Przemys{l}aw Kazienko This is my paper · ORCID

classification cs.CL

keywords aspecthierarchyRhetoricalStructureTheoryaspect-basedsentimentanalysisunsupervisedlearningdiscoursetreesConceptNetproductreviewscoverageevaluation

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that the hierarchical relationship between product aspects in reviews can be recovered without any labeled training data by reading the rhetorical structure of the text. The method treats each review as a discourse tree, replaces each discourse unit by the aspect it mentions, and keeps only nucleus-satellite edges; over many reviews, the most frequent directed aspect pairs are assembled into a hierarchy whose root is the product itself and whose deeper nodes are more specific features. The reported result is that the top 50 such pairs match human-coded ConceptNet relations with about 80% coverage, and every tested subset stays above 75%. A reader should care because hierarchical aspect structure is what would let sentiment systems answer questions like whether 'charging' refers to the battery or to the price.

What carries the argument

The load-bearing object is the Aspect Discourse Tree, built from a Rhetorical Structure Theory discourse tree by replacing each Elementary Discourse Unit with the aspect detected inside it and deleting nucleus-nucleus edges. A breadth-first search over every Aspect Discourse Tree generates directed tuples of the form (nucleus aspect, satellite aspect), and counting these tuples across all reviews produces a ranked list that is merged greedily into a hierarchy: a nucleus already present becomes the parent of the new satellite, and a missing nucleus starts a sub-hierarchy that may later be attached when a tuple links it to the main tree. This machinery converts a discourse-level claim, that a satellite specifies its nucleus, into a graph-level counting operation, and the aggregation over many documents is what lets noisy individual reviews average out.

What would settle it

Compute the same coverage after replacing each extracted nucleus-satellite pair with random aspect pairs drawn from the same reviews and matched in frequency; if the random baseline's coverage approaches 80%, the reported agreement is co-occurrence rather than discourse. A second decisive check is to compute coverage separately for each RST relation type and verify that elaboration-style relations carry the signal while contrast and sequence relations do not.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that discourse subordination tracks aspect specificity: in an RST relation, the nucleus is the more central part and the satellite is subsidiary, and when the two units mention different aspects, the satellite's aspect tends to be a feature or sub-aspect of the nucleus's aspect. Aggregating such pairs across 100,000 Amazon cell-phone reviews, the method builds an aspect tree, with phone at the root and children like case, battery, headset, bluetooth, and price, and with battery in turn as the parent of charge. The evaluation uses coverage $c=|A\cap C|/|A|$, where $A$ is the set of aspect hierarchy tuples generated by the method and $C$ is the set of ConceptNet hierarchical relations reachable in at most three hops. All measured subsets of the top aspect pairs score above 75%, with the top 5 at 100% and the top 50 at 80%.

Load-bearing premise

The method assumes that when one discourse unit is rhetorically subordinate to another, the aspect it mentions is a more specific sub-aspect of the nucleus aspect; if this specificity mapping fails, the extracted trees are just counted co-occurrences.

Editorial extensions

If this is right

Aspect hierarchies can be produced with no labeled training data, needing only a discourse segmenter and a noun-phrase extractor.
Sentiment can be attached at any depth of the hierarchy, so a system can report that 'charging' is a positive aspect of battery but also of price, depending on context.
Because the same aspect can appear as a child under several parents, the representation explicitly models context-dependent meaning rather than forcing each aspect into one node.
The stable 75–100% coverage across top-5 to top-50 pairs indicates that the discourse signal is not confined to the most frequent and obvious aspect pairs.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

I would not read 80% coverage as exact agreement: the ConceptNet reference set is closed under three hops and includes Synonym and RelatedTo, so the metric is a compatibility check, and a stricter one-hop test would likely score lower.
A direct test of the paper's hypothesis would separate RST relation types: if only elaboration-like relations carry specificity, filtering the tuple list by relation type should preserve coverage while shrinking the set; if all relation types contribute equally, the mechanism is closer to raw co-occurrence.
The same pipeline should transfer to other review domains, but the strength of the discourse signal may vary with text formality; testing on book or restaurant reviews would show whether the 80% figure is specific to hardware-like product descriptions.
The authors themselves note that there is no existing aspect hierarchy to compare against, which is why the evaluation uses ConceptNet as a proxy; building a small hand-labeled aspect hierarchy from the same reviews would turn the compatibility metric into a direct accuracy measure.

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Signed reviews

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Hi, quick take on arXiv:1909.01800. The novel bit is real: using RST discourse relations to build aspect hierarchies, aggregating across many reviews, is a combination I haven't seen. The pipeline is simple and the paper is honest about the difficulty of evaluation. That's the good news.

The problem is that the central evidence, the coverage metric, doesn't establish what they claim. Eq. (1) divides by an "all hierarchical tuples extracted from ConceptNet within a graph distance up to three hops" support set, which is extremely permissive: any two aspects connected by a path of length ≤3 through any of PartOf, HasA, IsA, MadeOf, LocatedNear, Synonym, RelatedTo counts. There's no check that the direction of the ConceptNet path matches the nucleus→satellite order in A, and no baseline, not even a frequency-matched random pair control. On a domain like phone accessories, the top 50 aspects are all generic and likely heavily connected in ConceptNet, so 80% coverage may just reflect semantic relatedness rather than RST-derived hierarchy. Since the whole claim is that discourse structure carries hierarchical info, this needs a comparison with a non-RST co-occurrence estimator. The authors say a comprehensive comparison is difficult (Sec. 4.2), which is fair, but that difficulty calls for a stronger evaluation, not just reporting an absolute number.

A secondary issue: Sec. 4 names only the EDU segmenter [20]; the component that labels RST relations and nuclearity, which Sec. 3.3 requires to generate the tuples, is not identified. As written, the pipeline is not reproducible. Also, the hierarchy construction in Sec. 3.3 is described in prose with an example, not a formal algorithm, so edge cases are unclear.

The positives: the paper frames the problem well, cites the relevant prior work (Joty et al., Kim et al.), and its own stated limitations are reasonable. The idea is worth pursuing; it just needs a proper evaluation.

My recommendation: send it to peer review, but with the expectation of major revision. A good reviewer would ask for a co-occurrence baseline, a direction-sensitive support set, a random control, and a fuller specification of the RST parser. If those are added, the result is worth knowing. As it stands, I wouldn't cite it for the empirical claim, but I'd keep it in mind as related work.

Referee Report

4 major / 5 minor

Summary. The paper proposes an unsupervised approach to extracting hierarchical aspect structures from product reviews using Rhetorical Structure Theory (RST). The method segments each review into elementary discourse units, detects noun-phrase aspects, replaces EDUs with the detected aspects, and then builds an Aspect Discourse Tree. The authors hypothesize that the nucleus-satellite relation between discourse units reflects a hierarchical (general-to-specific) relation between the corresponding aspects. All nucleus-satellite aspect pairs are collected across many documents and then assembled into a hierarchy by repeatedly adding satellites as children of their nuclei. The evaluation compares the most frequent aspect pairs against ConceptNet, counting a pair as correct if the two concepts are connected in ConceptNet within a graph distance of three hops, using relations such as PartOf, HasA, IsA, Synonym, and RelatedTo. The paper reports coverage values between 0.75 and 1.00 for the top 5 to 50 aspect pairs, and claims that the method generates aspect hierarchies consistent with human-generated hierarchies.

Significance. If the central claim were established, the paper would make a useful contribution: it offers a simple, unsupervised technique that connects discourse structure with aspect-based sentiment analysis, and the idea of deriving aspect hierarchies from RST relations is novel. The use of a large public Amazon review dataset and the emphasis on an unsupervised pipeline are also strengths. However, the current evaluation is not sufficient to support the claim. The reported coverage figures are computed against a broad and permissive ConceptNet support set, and no baselines or control conditions are provided. As a result, the evidence does not discriminate between the proposed RST-based method and a much simpler aspect co-occurrence model. The central claim is therefore plausible but not yet demonstrated; the paper requires a substantial additional evaluation effort to become convincing.

major comments (4)

[Sec. 4.2, Eq. (1)] The coverage metric uses a support set C that includes all concept pairs connected in ConceptNet by a path of up to three hops, with relation types including Synonym and RelatedTo, and with no requirement that the direction of the ConceptNet path match the nucleus-to-satellite order in the extracted tuple. This is a permissive oracle: frequently co-occurring product aspects (e.g., phone, battery, charger) are very likely to be within three hops in ConceptNet regardless of whether their discourse relation encodes specificity. Consequently, the reported 80% coverage on the top 50 pairs may simply reflect that the method's frequent pairs are generic semantic associates. To make the claim 'consistent with human-generated hierarchies' substantive, the authors should report (i) a random-pair baseline matched on aspect frequency, (ii) a co-occurrence-based aspect pair extractor as a non-RST baseline, and (iii) the coverage obtained when the ConceptNet path direction is required to align with the nucleus-satellite direction.
[Sec. 3.3] The hierarchy construction algorithm is described only in prose and is under-specified. It is not clear how the tuple list is ordered after aggregation (e.g., by raw frequency?), how cycles such as (case, phone) and (phone, case) are resolved, what happens when a satellite already has children, or how sub-hierarchies are merged when a later tuple connects two existing nodes. The example in Listing 1 and the textual walk-through do not uniquely define the output hierarchy. Moreover, the evaluation in Section 5 only checks pairwise coverage, never the correctness of the overall hierarchy (e.g., whether the final tree has intuitive root and depth structure). A formal algorithm (pseudocode) and an evaluation of the constructed hierarchy itself, not just its constituent pairs, are needed to substantiate the paper's title claim.
[Sec. 4] The experimental pipeline is not reproducible as described. The authors state that they used [20] for rhetorical segmentation and Spacy for noun-phrase extraction, but the component that assigns RST relation labels and determines nuclearity - which is essential for generating the nucleus-satellite tuples in Section 3.3 - is not identified anywhere in the paper. Without specifying the discourse parser (or the rule set used to infer nuclei and relations), a reader cannot reproduce the experiments or determine whether the results depend on a particular parser's accuracy. The authors should name the parser (e.g., a specific RST discourse parser and its version), the relation inventory, and how nuclearity was obtained.
[Sec. 5, Table 2] The coverage results are reported without any statistical context. The values for the top 20 to 50 range from 0.75 to 0.82 with no confidence intervals, no significance test, and no null model. Given the permissive nature of the support set, the null hypothesis 'randomly chosen aspect pairs from the same frequency distribution achieve similar coverage' may well hold. The authors should provide: (i) a frequency-matched random baseline with error bars, (ii) the exact list of the top 50 pairs and whether each is counted due to a direct relation, a multi-hop path, or a Synonym/RelatedTo bridge, and (iii) a test of whether coverage is significantly above the baseline.

minor comments (5)

[Abstract] The phrase 'proved to be consistently correct' is too strong for an evaluation based on coverage of a subset of pairs; suggest 'is consistent with' or 'agrees with'.
[Sec. 4.2] Typo: 'ConcepetNet' should be 'ConceptNet'.
[Sec. 5] Figure 4 is not described in the text; the reader cannot tell what aspects are shown or how the hierarchy was generated from the example.
[Sec. 1] The introduction does not state the contributions of the paper explicitly; a short list of contributions would help the reader.
[Sec. 4.2] The sentence 'the distance for antenna and telephone will be 2, because of the relations antenna PartOf receiver and receiver PartOf telephone' is a useful example but the text does not explain how the direction of the path is handled when computing coverage; please clarify.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the RST-derived aspect pairs are scored against an independent ConceptNet reference, and the 3-hop expansion, while permissive, does not make the evaluation an identity or a fitted prediction.

full rationale

The paper's central derivation is an unsupervised pipeline: RST discourse trees are parsed, EDUs are replaced by extracted aspects, nucleus-satellite pairs are counted, and the most frequent pairs seed a hierarchy. The evaluation compares these pairs (set A) against ConceptNet tuples (set C), which is an external, independently constructed knowledge graph. There is no equation in which an output is defined in terms of the reference set or in which a fitted parameter is later reported as a prediction. The 3-hop distance threshold in Eq. (1) is a broad inclusion criterion for ConceptNet, not a parameter fitted to the RST output; it may inflate coverage and weaken the benchmark, but it does not make A and C equivalent by construction. The authors' self-citation [1] is used for prior POS-based aspect extraction and as background, not as the load-bearing justification for the hierarchy claim. The main weakness is evidentiary rather than circular: no co-occurrence or random-pair baseline is provided, so the 80% coverage figure is not yet a clean demonstration that RST relations specifically encode aspect specificity. That concern belongs to correctness or experimental design, not to circularity.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The method relies on four main assumptions: the RST parser is reliable, noun phrases are the correct aspects, nucleus-satellite relations encode aspect hierarchies, and ConceptNet within 3 hops is a valid gold standard. The third is the central hypothesis of the paper and is not independently validated. No invented entities are introduced.

free parameters (1)

ConceptNet distance threshold = 3
Used to define the support set C for the coverage metric. Chosen because ConceptNet is sparse, without sensitivity analysis.

assumptions (4)

domain assumption The RST discourse parser (Feng and Hirst [20]) produces reliable discourse trees.
The pipeline depends on automatic discourse segmentation; parser errors propagate to aspect pair extraction.
domain assumption Noun phrases extracted from EDUs are the correct product aspects.
Aspect detection is performed solely by noun phrase extraction via Spacy's POS tagger, with no validation of whether these phrases are the intended aspects.
ad hoc to paper RST nucleus-satellite relations encode hierarchical aspect relationships.
This is the central hypothesis of the paper, introduced in Section 3 and not independently validated against human judgments.
domain assumption ConceptNet relations within up to three hops are a valid gold standard for aspect hierarchy correctness.
The evaluation uses ConceptNet's general knowledge graph as a proxy for human-generated hierarchies, with a permissive distance threshold.

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Cite this review

Pith. "Pith review of Extracting Aspects Hierarchies using Rhetorical Structure Theory." pith.science (2026). https://pith.science/paper/7XGWRFLV

@misc{pith2026190901800,
  author       = {Pith},
  title        = {Pith review of: Extracting Aspects Hierarchies using Rhetorical Structure Theory},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7XGWRFLV}},
  note         = {Machine review of arXiv:1909.01800}
}

read the original abstract

We propose a novel approach to generate aspect hierarchies that proved to be consistently correct compared with human-generated hierarchies. We present an unsupervised technique using Rhetorical Structure Theory and graph analysis. We evaluated our approach based on 100,000 reviews from Amazon and achieved an astonishing 80% coverage compared with human-generated hierarchies coded in ConceptNet. The method could be easily extended with a sentiment analysis model and used to describe sentiment on different levels of aspect granularity. Hence, besides the flat aspect structure, we can differentiate between aspects and describe if the charging aspect is related to battery or price.

Figures

Figures reproduced from arXiv: 1909.01800 by the authors.

**Figure 2.** An Exemplary Discourse Tree Based on Rhetorical Structure Theory. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** An exemplary Aspect Hierarchical Relation extracted from a single Aspect Discourse Tree. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Aspect Hierarchy extracted from Amazon Cell Phones and Accessories dataset. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [20]

Two-pass discourse segmentation with pairing and global features

Vanessa Wei Feng and Graeme Hirst. Two-pass discourse segmentation with pairing and global features. CoRR, abs/1407.8215, 2014. 7 Extracting Aspects Hierarchies using Rhetorical Structure Theory A PREPRINT

arXiv 2014

[2] [1]

Method for aspect-based sentiment annotation using rhetorical analysis

Łukasz Augustyniak, Krzysztof Rajda, and Tomasz Kajdanowicz. Method for aspect-based sentiment annotation using rhetorical analysis. In Ngoc Thanh Nguyen, Satoshi Tojo, Le Minh Nguyen, and Bogdan Trawi´nski, editors, Intelligent Information and Database Systems, pages 772–781, Cham, 2017. Springer International Publishing

work page 2017

[3] [2]

A Bayesian Model for Joint Unsupervised Induction of Sentiment, Aspect and Discourse Representations

Angeliki Lazaridou, Ivan Titov, and Caroline Sporleder. A Bayesian Model for Joint Unsupervised Induction of Sentiment, Aspect and Discourse Representations. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1630–1639, 2013

work page 2013

[4] [3]

Discourse indicators for content selection in summarization

Annie Louis, Aravind Joshi, and Ani Nenkova. Discourse indicators for content selection in summarization. In Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue , SIGDIAL ’10, pages 147–156, Stroudsburg, PA, USA, 2010. Association for Computational Linguistics

work page 2010

[5] [4]

Using Discourse Structure Improves Ma- chine Translation Evaluation

Francisco Guzm ´an, Shaﬁq Joty, Lluis M `arquez, and Preslav Nakov. Using Discourse Structure Improves Ma- chine Translation Evaluation. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 687–698, 2014

work page 2014

[6] [5]

J.R. Martin. English Text. John Benjamins Publishing Company, Amsterdam, nov 1992

work page 1992

[7] [6]

D-STAG: A formalism for discourse analysis based on SDRT and using synchronous TAG

Laurence Danlos. D-STAG: A formalism for discourse analysis based on SDRT and using synchronous TAG. Lecture Notes in Computer Science (including subseries Lecture Notes in Artiﬁcial Intelligence and Lecture Notes in Bioinformatics), 5591 LNAI:64–84, 2011

work page 2011

[8] [7]

Rhetorical structure theory: Toward a functional theory of text organization

William C Mann and Sandra A Thompson. Rhetorical structure theory: Toward a functional theory of text organization. Text, 8(3):243–281, 1988

work page 1988