Sentence Embeddings as an intermediate target in end-to-end summarisation

Maciej Zembrzuski; Saad Mahamood

REVIEW 5 major objections 7 minor 33 references

Sentence Embeddings as an intermediate target in end-to-end summarisation

T0 review · 5 major / 7 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Predicting a summary's sentence embedding, rather than word-level selection probabilities, improves end-to-end hotel review summarisation when source and target text are only loosely aligned.

desk verdict Genuinely novel embedding-targeted extractor, but the end-to-end comparison is confounded and the 'prove' claim overreaches. read the letter →

arxiv 2505.03481 v1 pith:Q2CL7MX4 submitted 2025-05-06 cs.CL

Maciej Zembrzuski , Saad Mahamood This is my paper · ORCID

classification cs.CL

keywords abstractivesummarizationextractivesentenceembeddingsUniversalEncodercontentselectionweaklyalignedcorporahotelreviewend-to-end

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 proposes USESUM, a two-stage system that first selects three sentences from a long set of hotel reviews and then generates a one-sentence summary with an abstractive Transformer. Its central claim is that the extractive selector should be trained to predict the sentence embedding of the target summary, and that this intermediate target works better for loosely aligned corpora than predicting sentence-selection probabilities. On the paper's hotel-review dataset, where only about a third of summaries overlap lexically with the reviews, USESUM outperforms the compared extractive-plus-abstractive systems on BLEU, ROUGE-L, and METEOR, and receives the highest human scores for semantic coverage. The result matters because it offers a way to train content selection without word-level alignment labels, a common bottleneck when summarising opinion and review text.

What carries the argument

The load-bearing mechanism is the angle-drop objective built on a Maximal Marginal Relevance-style update. At step $i$, the model keeps a running estimate $\hat{y}_{i-1}$ of the summary embedding; after reading input embedding $x_i$, it updates to $\hat{y}_i = \hat{y}_{i-1} - \mathrm{score}\cdot(\hat{y}_{i-1} - x_i)$, and the training loss is $\mathrm{acos}(\hat{y}_{i-1},\mathrm{target}) - \mathrm{acos}(\hat{y}_i,\mathrm{target})$, the reduction in arccosine distance to the target. This objective is what lets weakly aligned training pairs still teach sentence ranking: a sentence that moves the predicted embedding closer to the target, even if it is not identical to it, receives positive gradient. The abstractive Transformer with copy attention then turns the three selected sentences into the final summary.

What would settle it

Train the same end-to-end architecture on a held-out split of the hotel-review dataset, but replace the embedding-regression objective with a sentence-selection probability objective over the same Universal Sentence Encoder embeddings; if the probability-based selector matches or beats USESUM's BLEU, ROUGE-L, and METEOR scores, the paper's central claim that embedding prediction is the better intermediate target is refuted.

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Extended reading notes

Core claim

The paper argues and demonstrates that the angle between pre-trained sentence embeddings can serve as the training signal for extractive content selection in an end-to-end summariser. In USESUM, a bi-directional LSTM reads sentence embeddings produced by the Universal Sentence Encoder, an LSTM-style decoder updates a predicted summary embedding by moving it toward each candidate sentence proportionally to a learned score, and the loss at each step is the reduction in arcus-cosine distance to the target summary embedding. The extractor then chooses the three input sentences whose embeddings are closest to the final predicted embedding and passes them to a Transformer abstractive model. The paper's experiments on hotel-review data show that this embedding-prediction objective leads to better end-to-end summaries than word-level sentence-selection probabilities used by the compared extractive systems, and the human evaluation shows the generated summaries more often cover the target semantics.

Load-bearing premise

The load-bearing premise is that the angle between Universal Sentence Encoder embeddings measures how much of the target summary's content a source sentence carries, so that reducing that angle toward a predicted summary embedding selects sentences that the abstractive model can use.

Editorial extensions

If this is right

The comparison on the hotel-review data shows that an embedding-prediction extractor can outperform word-level sentence-selection extractors in end-to-end BLEU, ROUGE-L, and METEOR when source and target have low lexical overlap.
Weakly aligned source-target pairs, which cannot be used to train a sentence-selection classifier, can still contribute training signal to the embedding-regression extractor.
The extractor reduces the input to three sentence embeddings, making the abstractive stage independent of the original document length, up to 800 sentences in the dataset.
The paper's human evaluation shows that the improvement in automatic metrics is accompanied by higher semantic coverage, not merely higher lexical overlap.
The beam-search named-entity promotion step improves all compared end-to-end systems on the dataset.

Reading between the lines

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

If the mechanism is real, a natural extension is to make the number of selected sentences learnt or adaptive rather than fixed at three; the current system hard-codes three based on the LEAD-3 heuristic.
The paper's observation that cosine similarity rewards averaging suggests that a diversity-aware or coverage-aware variant of the angle-drop objective might close the gap to the BOTTOM-UP system's cosine score while keeping the semantic advantage.
A direct test of the central claim would be to replace the Universal Sentence Encoder with a different sentence-embedding model; if the advantage over word-level selection disappears, the effect is encoder-specific rather than a general property of embedding targets.
For multilingual review summarisation, language-agnostic sentence encoders could make the same weakly aligned training strategy applicable without parallel sentence-level data.

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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

The one thing to know: this paper has a genuinely new technical idea—training the extractive selector to regress on the target summary's sentence embedding under an incremental angle loss—and it ships code and data. But the end-to-end comparison is too confounded to support the headline claim, and the 'prove' in the abstract is not earned.

What is new and right: USESUM's extractor predicts a vector in Universal Sentence Encoder space and selects the three input sentences closest to it; the loss in Eq. 11 is the change in arc-cosine distance to the target at each step. I'm not aware of that objective in the summarization literature. The USEG dataset is well chosen for this problem because the low overlap between reviews and single-sentence summaries is exactly where word-level alignment fails. The authors also deserve credit for releasing the implementation and results, and for reporting the extractive and end-to-end stages separately.

Where it gets soft: the evidence base is thin. The baselines are from 2018, the metric gaps are small—BLEU 0.0225 vs 0.0208, ROUGE-L 0.1479 vs 0.1427—and there are no significance tests or confidence intervals. Human evaluation on 100 outputs per system shows USESUM at 60% grammatical versus 91% for BOTTOM-UP, which makes the improvement in automatic metrics less reassuring. More important is the confounding the stress-test note flagged: the abstractive Transformer was trained on sentences selected by the same embedding-angle rule USESUM uses. Feeding that model with the outputs of NEUSUM or BERTSUM is a distribution mismatch, so part of USESUM's gain could be that its inputs match training, not that the selection objective is superior. The paper does not address this.

The deeper worry about the angle-to-information monotonicity is also legitimate. The model's entire learning signal is the change in angle to the target embedding, yet the paper itself concedes in Section 6.5 that cosine similarity can be misleading and can 'overly promote' systems that simply average document vectors. No direct validation that decreasing angle corresponds to selecting more informative sentences is provided. That softens the central claim, though it doesn't sink the engineering result.

The abstract's 'prove' should be softened to 'demonstrate on a single dataset' or similar. The authors already state that adapting to other datasets requires major architectural changes, so the claim is narrow by their own admission.

Overall, this is a coherent, honest engineering paper with a novel intermediate objective and a useful dataset. It is not a proof, and the empirical support is not yet solid enough to accept as-is. A serious referee should see it, but the revision needs significance testing, modern baselines, and an abstractive training setup that does not advantage USESUM by construction.

I'd send it to review with a request for major revision; I wouldn't cite it in its current form unless I were working on this exact dataset.

Referee Report

5 major / 7 minor

Summary. The paper proposes USESUM, a two-stage summarisation system for hotel review data (USEG). In the first stage, an extractive LSTM with a gated update rule is trained to predict the Universal Sentence Encoder (USE) embedding of the target summary sentence, using as loss the change in arccos distance between the predicted embedding and the target embedding (Eq. 11). At inference, the three input sentences whose USE embeddings have the smallest angle to the predicted embedding are selected. In the second stage, an abstractive Transformer (customised OpenNMT with copy attention, focal loss, and beam-search entity promotion) generates the final summary from these three sentences. The paper compares USESUM against BASELINE, BERTSUM, NEUSUM, REFRESH, and BOTTOM-UP on USEG, reporting that USESUM achieves the highest BLEU, ROUGE-L, and METEOR in the end-to-end experiment (Table 2), with a human evaluation showing higher semantic coverage but much lower grammaticality than BOTTOM-UP. The authors argue that predicting sentence-level embeddings is a better intermediate objective than predicting sentence-selection probabilities when source and target have low lexical overlap.

Significance. If the results are correct, the idea of using pre-trained sentence embeddings as an intermediate regression target for content selection is a plausible and potentially useful contribution for summarisation tasks with large, loosely aligned inputs. The paper ships code and models, uses a real-world public dataset, and includes both automatic and human evaluation. The main scientific claim—that embedding prediction outperforms word-level selection probabilities for weakly aligned corpora—is interesting and testable. However, the current empirical support is not conclusive because of a design confound in the end-to-end comparison, the lack of statistical testing on very small margins, and the absence of direct validation of the angle-to-information monotonicity that the learning objective assumes. The strengths of code release and a non-standard dataset are appreciated, but the load-bearing evidence needs substantial strengthening before the central claim can be accepted.

major comments (5)

[Section 4.3 and Section 6.2] The abstractive Transformer is trained on the three sentences selected by angle similarity to the target embedding (the oracle selection described in Section 4.3), but the end-to-end evaluation in Table 2 feeds this same model with sentences selected by NEUSUM, BERTSUM, REFRESH, and BASELINE. Those inputs come from a different distribution than the training data, so the observed BLEU, ROUGE-L, and METEOR advantage of USESUM may reflect the abstractive model's specialization to its training distribution rather than a general superiority of the embedding-prediction objective. A fair comparison would train a separate abstractive model for each extractive system, or train one abstractive model on a mixture of selections from all systems so that the extractive component is the only variable.
[Section 4.2.1 and Section 6.5] The training signal in Eq. (11) assumes that decreasing the angle between the predicted embedding and the target embedding is a monotone proxy for accumulating summary-relevant information. Section 6.5 concedes that angle similarity can be misleading and can 'overly promote' the BASELINE, which simply averages document vectors. This concession directly undermines the central mechanism unless the monotonicity is validated on the USEG domain; the paper provides no oracle-style experiment showing that sentences selected by minimal angle to the target embedding yield higher end-to-end ROUGE than sentences selected by other scoring functions. The reader's strongest concern is that the extractive signal may reward embedding averaging rather than content selection.
[Table 2 and Section 6.3] No significance tests, confidence intervals, or error bars are reported anywhere in the results. The BLEU difference between USESUM (0.0225) and NEUSUM (0.0208) is small in absolute terms, and the human evaluation covers only 100 outputs per system, with USESUM at 60% grammaticality versus 91% for BOTTOM-UP. Given the small automatic-metric margins and the manually tuned beam-search factors, the paper's claim of consistent superiority is not statistically supported. The authors should report variance or significance tests and discuss the grammar gap in the human evaluation more prominently.
[Section 5, BERTSUM paragraph] BERTSUM is restricted to 512 input tokens while the other systems process up to 800 sentences, so its low performance is partly an artifact of the input limit rather than a comparison of summarisation quality. The comparison should either adapt BERTSUM to handle the full input (as is done for NEUSUM) or report results on a subset of documents for which all models receive the same input length; otherwise the baseline is not on equal footing.
[Section 6.1] The extractive evaluation in Table 1 reports only the first selected sentence, whereas the abstractive component in Section 4.3 uses the top three sentences. To support the claim that the embedding-based selector is better for the end-to-end task, the paper should compare the quality of the three-sentence selections across extractors (e.g., ROUGE-L or METEOR of the selected set against the target summary) and show that the USESUM-selected set is indeed more informative. Without this, the connection between the extractive objective and the end-to-end result remains indirect.

minor comments (7)

[Throughout] There are several typos and notation inconsistencies: 'METOR' for METEOR in Section 5, 'NEOSUM' for NEUSUM in Table 2, 'week alignment' for weak alignment in Section 4.2.1, 'challange' in Section 3, and '0,5115' with a comma decimal in Table 2.
[Abstract] The word 'prove' is too strong for an empirical study; 'demonstrate' or 'provide evidence that' would be more appropriate, since the paper presents experimental results rather than a mathematical proof.
[Section 3] Dataset statistics are missing: the number of hotels, the train/validation/test split sizes, and the average number of reviews and sentences per hotel should be reported for reproducibility and context.
[Section 4.3] The hyperparameters of the abstractive Transformer (number of layers, hidden sizes, training steps, batch size, learning rate) are not fully specified; only some features are described, which makes replication difficult.
[Section 6.2] The candidate-selection heuristic that compares each candidate sentence embedding with the embedding of the three input sentences (or the whole document for BOTTOM-UP) should be described precisely, as it affects the reported scores and may interact with the USE embedding space in a way that favors USESUM.
[References] The reference 'Lew, 2004' in Section 3 should be 'Lin, 2004' for ROUGE, and the reference for Yang (2016) is a technical report without a formal venue; please check the citation format.
[Figure 1] Figure 1 is referenced in Section 4 but is not visible in the manuscript text; please ensure the figure is included and referenced correctly.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the headline claims are measured with word-overlap and human metrics external to the USE embedding space used for training and selection.

full rationale

USESUM's extractor is trained with Eq. 11, loss_i = acos(yhat_{i-1}, target) - acos(yhat_i, target), which is the negative of the Eq. 1 information-gain definition; this is a coherent training objective, not a hidden reuse of the evaluation quantity. The abstractive decoder is trained on oracle sentences selected by angle to the target embedding, and at inference USESUM selects by angle to its predicted embedding, so the end-to-end comparison shares a selection distribution with the decoder's training data; this is a fairness and robustness concern about distribution shift for NEUSUM, BERTSUM, REFRESH, and BASELINE, but it does not make the BLEU, ROUGE-L, METEOR, or human-evaluation numbers equivalent to the training target by construction. The cosine-similarity evaluation metric is computed in the same Universal Sentence Encoder space used by the extractor, and Section 6.5 explicitly concedes that this metric can be misleading and overly promotes the averaging BASELINE; because the paper's main claims rest on external word-overlap metrics and human semantic ratings, this internal-metric circularity is not load-bearing. There are no self-citations, imported uniqueness theorems, or ansatz-by-citation steps. The abstract's word 'prove' overstates what is an empirical demonstration, but overclaiming is a correctness issue, not a circular derivation.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The central claim relies on the pre-trained Universal Sentence Encoder providing a transferable semantic geometry, on the MMR-style assumption that marginal angle reduction equals information gain, and on the USEG dataset retaining enough source-target signal. The manual beam-search factors are fitted on validation data.

free parameters (3)

Beam-search unseen-entity penalty factor = 50
Section 4.3: penalty for named entities not present in the source text, estimated manually on the validation set.
Beam-search source-entity promotion factor = 0.4
Section 4.3: promotion for named entities present in the source text, estimated manually on the validation set.
Number of extracted sentences K = 3
Section 4: chosen a priori, inspired by the LEAD-3 baseline, not tuned on the USEG dataset.

assumptions (3)

domain assumption Universal Sentence Encoder embeddings are semantically meaningful for hotel review text such that cosine angle reflects content similarity.
The entire selection mechanism (Eqs. 1-8) assumes the angle between USE embeddings measures information gain; cited from Cer et al. 2018 but not validated on this corpus.
domain assumption Maximal Marginal Relevance (MMR) improves extractive summarization.
Section 4.1: the model follows Carbonell and Goldstein's MMR; this is taken from prior literature.
domain assumption Source reviews contain recoverable semantic signal for the target USP summary for a majority of examples.
Section 3 reports only a third of summaries have sufficient ROUGE overlap and a third have no reflected information, yet these examples are used for training; the paper assumes the embedding similarity captures weak alignment.

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Cite this review

Pith. "Pith review of Sentence Embeddings as an intermediate target in end-to-end summarisation." pith.science (2026). https://pith.science/paper/Q2CL7MX4

@misc{pith2026250503481,
  author       = {Pith},
  title        = {Pith review of: Sentence Embeddings as an intermediate target in end-to-end summarisation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Q2CL7MX4}},
  note         = {Machine review of arXiv:2505.03481}
}

read the original abstract

Current neural network-based methods to the problem of document summarisation struggle when applied to datasets containing large inputs. In this paper we propose a new approach to the challenge of content-selection when dealing with end-to-end summarisation of user reviews of accommodations. We show that by combining an extractive approach with externally pre-trained sentence level embeddings in an addition to an abstractive summarisation model we can outperform existing methods when this is applied to the task of summarising a large input dataset. We also prove that predicting sentence level embedding of a summary increases the quality of an end-to-end system for loosely aligned source to target corpora, than compared to commonly predicting probability distributions of sentence selection.

Figures

Figures reproduced from arXiv: 2505.03481 by the authors.

**Figure 1.** USESUM system diagram for generating USP abstractive summaries the USESUM setting will be further processed by the abstractive summarisation system. During the training phase of the extractive system, the sentence embedding of the reference summary y and the embedding xi of the i − th processed sentence are given. Both y and xi are semantically meaningful sentence embeddings, calculated by Universal Sentence En… view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

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ENTRY address author booktitle chapter edition editor howpublished institution journal key month note number organization pages publisher school series title type volume year eprint doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRINGS urlintro eprinturl eprintpr...
[2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...
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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [1]

URL: " 'urlintro :=

ENTRY address author booktitle chapter edition editor howpublished institution journal key month note number organization pages publisher school series title type volume year eprint doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRINGS urlintro eprinturl eprintpr...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

[3] [3]

Jaime Carbonell and Jade Goldstein. 1998. The use of MMR , diversity-based reranking for reordering documents and producing summaries. In SIGIR '98 Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, pages 335--336, Melbourne, Australia. Association for Computing Machinery

work page 1998

[4] [4]

Asli Celikyilmaz, Antoine Bosselut, Xiaodong He, and Yejin Choi. 2018. Deep communicating agents for abstractive summarization. In Proceedings of the NAACL Conference

work page 2018

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John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Yun-Hsuan Sung, Brian Strope, and Ray Kurzweil

Daniel Cer, Yinfei Yang, Sheng yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Yun-Hsuan Sung, Brian Strope, and Ray Kurzweil. 2018. Universal sentence encoder. arXiv preprint arXiv:1803.11175

arXiv 2018

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Jacob Devlin, Ming - Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805

arXiv 2018

[8] [8]

Bonnie Dorr, David Zajic, and Richard Schwartz. 2003. Hedge trimmer: A parse-and-trim approach to headline generation. In Proceedings of the HLT-NAACL 03 on Text Summarization Workshop - Volume 5, HLT-NAACL-DUC '03, pages 1--8, Stroudsburg, PA, USA. Association for Computational Linguistics

work page 2003