Compressive Summarization with Plausibility and Salience Modeling

EMNLP 2020, pp. 6259-6274, 2020.

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We present a compressive summarization system that decomposes span-level compression into two learnable objectives, plausibility and salience, on top of a minimal set of rules derived from a constituency tree

Abstract:

Compressive summarization systems typically rely on a seed set of syntactic rules to determine under what circumstances deleting a span is permissible, then learn which compressions to actually apply by optimizing for ROUGE. In this work, we propose to relax these explicit syntactic constraints on candidate spans, and instead leave the de...More

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Introduction
  • Compressive summarization systems offer an appealing tradeoff between the robustness of extractive models and the flexibility of abstractive models.
  • In order to learn plausibility, the authors leverage a pre-existing sentence compression dataset (Filippova and Altun, 2013); the model learned from this data transfers well to the summarization settings the authors consider
  • Using these two models, the authors build a pipelined compressive system as follows: (1) an off-the-shelf extractive model highlights important sentences; (2) for each sentence, high-recall compression rules yield span candidates; (3) two pre-trained Transformer models (Clark et al, 2020) judge the plausibility and salience of spans, respectively, and only spans
Highlights
  • Compressive summarization systems offer an appealing tradeoff between the robustness of extractive models and the flexibility of abstractive models
  • In order to learn plausibility, we leverage a pre-existing sentence compression dataset (Filippova and Altun, 2013); our model learned from this data transfers well to the summarization settings we consider
  • By finetuning salience with only 500 in-domain samples, we demonstrate our compressive system can match or exceed the ROUGE of an in-domain extractive model trained on tens of thousands of documentsummary pairs
  • We describe our compressive summarization system that leverages our notions of plausibility and salience
  • 6.1 Benchmark Results the accident happened in santa ynez california, near where crosby lives. crosby was driving at approximately 50 mph when he struck the jogger, according to california highway patrol spokesman don clotworthy. the jogger suffered multiple fractures, and was airlifted to a hospital in santa barbara, clotworthy said
  • We present a compressive summarization system that decomposes span-level compression into two learnable objectives, plausibility and salience, on top of a minimal set of rules derived from a constituency tree
Methods
Results
  • Compression consistently improves ROUGE, even when coupled with a strong extractive model.
  • Gains are especially pronounced on datasets with more abstractive summaries, where applying compression roughly adds +2 ROUGE-1; the authors note there is a large gap between extractive and abstractive approaches on tasks like XSum due to the amount of paraphrasing in reference summaries (Narayan et al, 2018).
  • The authors' system outperforms strong extractive models on these datasets, and yields opening statements in the murder trial of movie theater massacre suspect james holmes are scheduled for april 27, more than a month ahead of schedule, a colorado court spokesman said.
  • The summaries are highly compressive: spans not contributing to the main event or story are deleted, while maintaining grammaticality and factuality
Conclusion
  • The authors present a compressive summarization system that decomposes span-level compression into two learnable objectives, plausibility and salience, on top of a minimal set of rules derived from a constituency tree.
  • Experiments across both in-domain and out-of-domain settings demonstrate the approach outperforms strong extractive baselines while creating well-formed summaries
Summary
  • Introduction:

    Compressive summarization systems offer an appealing tradeoff between the robustness of extractive models and the flexibility of abstractive models.
  • In order to learn plausibility, the authors leverage a pre-existing sentence compression dataset (Filippova and Altun, 2013); the model learned from this data transfers well to the summarization settings the authors consider
  • Using these two models, the authors build a pipelined compressive system as follows: (1) an off-the-shelf extractive model highlights important sentences; (2) for each sentence, high-recall compression rules yield span candidates; (3) two pre-trained Transformer models (Clark et al, 2020) judge the plausibility and salience of spans, respectively, and only spans
  • Methods:

    The authors' experiments use the following English datasets: CNN/DailyMail (Hermann et al, 2015), CNN, New York Times (Sandhaus, 2008), WikiHow (Koupaee and Wang, 2018), XSum (Narayan et al, 2018), and Reddit (Kim et al, 2019).8.
  • CNN WikiHow XSum Reddit Type Model.
  • R1 R2 RL R1 R2 RL R1 R2 RL R1 R2 RL ext Lead-k.
  • 29.80 11.40 26.45 24.96 5.83 23.23 17.02 2.72 13.79 19.64 2.40 14.79 ext BERTSum.
  • — — — 30.31 8.71 28.24 22.86 4.48 17.16 23.86 5.85 19.11 ext MatchSum♦.
  • The authors benchmark the system first with an automatic evaluation based on ROUGE-1/2/L F1 (Lin, 2004).7 The authors' experiments use the following English datasets: CNN/DailyMail (Hermann et al, 2015), CNN, New York Times (Sandhaus, 2008), WikiHow (Koupaee and Wang, 2018), XSum (Narayan et al, 2018), and Reddit (Kim et al, 2019).8
  • Results:

    Compression consistently improves ROUGE, even when coupled with a strong extractive model.
  • Gains are especially pronounced on datasets with more abstractive summaries, where applying compression roughly adds +2 ROUGE-1; the authors note there is a large gap between extractive and abstractive approaches on tasks like XSum due to the amount of paraphrasing in reference summaries (Narayan et al, 2018).
  • The authors' system outperforms strong extractive models on these datasets, and yields opening statements in the murder trial of movie theater massacre suspect james holmes are scheduled for april 27, more than a month ahead of schedule, a colorado court spokesman said.
  • The summaries are highly compressive: spans not contributing to the main event or story are deleted, while maintaining grammaticality and factuality
  • Conclusion:

    The authors present a compressive summarization system that decomposes span-level compression into two learnable objectives, plausibility and salience, on top of a minimal set of rules derived from a constituency tree.
  • Experiments across both in-domain and out-of-domain settings demonstrate the approach outperforms strong extractive baselines while creating well-formed summaries
Tables
  • Table1: Results on CNN, WikiHow, XSum, and Reddit. Our system consistently achieves higher ROUGE than extraction-only baselines. Additionally, our system achieves higher ROUGE-L than PEGASUSBASE on WikiHow and Reddit without summarization-specific pre-training. ♦Extractive SOTA; ♥Abstractive SOTA. CNN, WikiHow, XSum, Reddit) and 2 (CNN/DM) show ROUGE results. From these tables, we make the following observations. Training, development, and test dataset sizes for CNN/Daily Mail (<a class="ref-link" id="cHermann_et+al_2015_a" href="#rHermann_et+al_2015_a">Hermann et al, 2015</a>), CNN (subset of CNN/DM), New York Times (<a class="ref-link" id="cSandhaus_2008_a" href="#rSandhaus_2008_a">Sandhaus, 2008</a>), XSum (<a class="ref-link" id="cNarayan_et+al_2018_a" href="#rNarayan_et+al_2018_a">Narayan et al, 2018</a>), WikiHow (<a class="ref-link" id="cKoupaee_2018_a" href="#rKoupaee_2018_a">Koupaee and Wang, 2018</a>), and Reddit (<a class="ref-link" id="cKim_et+al_2019_a" href="#rKim_et+al_2019_a">Kim et al, 2019</a>). For each dataset, the extraction model selects the top-k sentences to form the basis of the compressive summary
  • Table2: Results on CNN/DM. Notably, a pipeline with MatchSum (Zhong et al, 2020) extraction and our compression module achieves state-of-the-art ROUGE1. ♦Extractive SOTA; ♥Abstractive SOTA. Training hyperparameters for the extraction and compression models (§3)
  • Table3: CUPS-produced summaries on CNN, where strikethrough text implies the span is deleted as judged by the plausibility and salience models. The base sentences before applying compression are derived from CUPSEXT, the sentence extractive model. BERT- and ELECTRA-based system hyperparameters for the plausibility (§2.1) and salience models (§2.2). We fix the plausibility threshold at 0.6 and only optimize the salience thresold
  • Table4: Human evaluation of grammaticality (G) and factuality (F) of summaries, comparing the precision of span deletions from our compression rules (§2.3) before and after applying the plausibility model (§2.1). Annotator agreement for grammaticality and factuality studies on CNN and Reddit. Values displayed are computed using Krippendorff’s α (<a class="ref-link" id="cKrippendorff_1980_a" href="#rKrippendorff_1980_a">Krippendorff, 1980</a>)
  • Table5: Results on out-of-domain transfer tasks. Fine-tuning results are averaged across 5 runs, each with a random batch of 500 target domain samples. Variance among these runs is very low; see Appendix H. Results on CNN/DM, CNN, WikiHow, XSum, and Reddit with initializing the pre-trained encoders in CUPS to BERTBASE as opposed to ELECTRABASE. CNN/DM, CNN, WikiHow, XSum, Reddit) shows results using BERTBASE as the pretrained encoder. While the absolute ROUGE
  • Table6: Results on WikiHow, XSum, and Reddit with replacing CUPSEXT with MatchSum (Zhong et al, 2020), a state-of-the-art extractive model
  • Table7: Results on CNN, WikiHow, XSum, and Reddit with removing the plausibility model in CUPSCMP
  • Table8: Results on NYT → CNN, reporting ROUGE with standard deviation across 5 independent runs with a random batch of 500 samples
  • Table9: Results on CNN → Reddit, reporting ROUGE with standard deviation across 5 independent runs with a random batch of 500 samples
  • Table10: Results on XSum → WikiHow, reporting ROUGE with standard deviation across 5 independent runs with a random batch of 500 samples
  • Table11: Results on NYT → CNN, CNN → Reddit, and XSum → WikiHow after removing the salience model
  • Table12: Results on the development sets of CNN/DM, CNN, WikiHow, XSum, and Reddit using the default CUPS system, leveraging both BERTBASE and ELECTRABASE pre-trained encoders
  • Table13: Number of training steps and total time elapsed for training extraction and compression models on CNN/DM, CNN, NYT, WikiHow, XSum, Reddit, and Google*. Models are benchmarked on a 32GB NVIDIA V100 GPU. *Google refers to the sentence compression dataset released by <a class="ref-link" id="cFilippova_2013_a" href="#rFilippova_2013_a">Filippova and Altun (2013</a>), which is only used to train the plausibility compression model
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Related work
  • Compressive Summarization. Our work follows in a line of systems that use auxiliary training data or objectives to learn sentence compression (Martins and Smith, 2009; Woodsend and Lapata, 2012; Qian and Liu, 2013). Unlike these past approaches, our compression system uses both a plausibility model optimized for grammaticality and a salience model optimized for ROUGE. Almeida and Martins (2013) leverage such modules and learn them jointly in a multi-task learning setup, but face an intractable inference problem in their model which needs sophisticated approximations. Our approach, by contrast, does not need such approximations or expensive inference machinery like ILP solvers (Martins and Smith, 2009; Berg-Kirkpatrick et al, 2011; Durrett et al, 2016). The highly decoupled nature of our pipelined compressive system is an advantage in terms of training simplicity: we use only simple MLE-based objectives for extraction and compression, as opposed to recent compressive methods that use joint training (Xu and Durrett, 2019; Mendes et al, 2019) or reinforcement learning (Zhang et al, 2018). Moreover, we demonstrate our compression module can stack with state-of-the-art sentence extraction models, achieving additional gains in ROUGE.
Funding
  • This work was partially supported by NSF Grant IIS-1814522, NSF Grant SHF-1762299, a gift from Salesforce Inc., and an equipment grant from NVIDIA
  • Results presented in this paper were obtained using the Chameleon testbed supported by the National Science Foundation
Study subjects and analysis
samples: 500
When integrated into a simple extraction-compression pipeline, our method achieves strong in-domain results on benchmark summarization datasets, and human evaluation shows that the plausibility model generally selects for grammatical and factual deletions. Furthermore, the flexibility of our approach allows it to generalize cross-domain: our system fine-tuned on only 500 samples from a new domain can match or exceed an in-domain extractive model trained on much more data.1. We benchmark our system first with an automatic evaluation based on ROUGE-1/2/L F1 (Lin, 2004).7

samples: 500
When integrated into a simple extraction-compression pipeline, our method achieves strong in-domain results on benchmark summarization datasets, and human evaluation shows that the plausibility model generally selects for grammatical and factual deletions. Furthermore, the flexibility of our approach allows it to generalize cross-domain: our system fine-tuned on only 500 samples from a new domain can match or exceed an in-domain extractive model trained on much more data.1. Compressive summarization systems offer an appealing tradeoff between the robustness of extractive models and the flexibility of abstractive models

in-domain samples: 500
Our experiments consist of three transfer tasks, which mimic real-world domain shifts (e.g., newswire → social media). By finetuning salience with only 500 in-domain samples, we demonstrate our compressive system can match or exceed the ROUGE of an in-domain extractive model trained on tens of thousands of documentsummary pairs. 2 Plausible and Salient Compression

randomly sampled documents: 100
We analyze (1) our default system CUPS, which deletes spans ZP ∩ZS; and (2) a variant CUPS-NOPL (without plausibility but with salience), which only deletes spans ZS, to specifically understand what compressions the salience model makes without the plausibility model’s guardrails. Using 100 randomly sampled documents from CNN, we conduct a series of experiments detailed below. On average, per sentence, 16% of candidate spans deleted by the salience model alone are not plausible

samples: 500
This increase is largely due to compression improving ROUGE precision: extraction is adept at retrieving content-heavy sentences with high recall, and compression helps focus on salient content within those sentences. More importantly, we see that performance via fine-tuning on 500 samples matches or exceeds in-domain extraction ROUGE. On NYT → CNN and CNN → Reddit, our system outperforms in-domain extraction baselines (trained on tens of thousands of examples), and on XSum → WikiHow, it comes within 0.3 in-domain average ROUGE

target domain samples: 500
Human evaluation of grammaticality (G) and factuality (F) of summaries, comparing the precision of span deletions from our compression rules (§2.3) before and after applying the plausibility model (§2.1). Annotator agreement for grammaticality and factuality studies on CNN and Reddit. Values displayed are computed using Krippendorff’s α (Krippendorff, 1980). Results on out-of-domain transfer tasks. Fine-tuning results are averaged across 5 runs, each with a random batch of 500 target domain samples. Variance among these runs is very low; see Appendix H. Results on CNN/DM, CNN, WikiHow, XSum, and Reddit with initializing the pre-trained encoders in CUPS to BERTBASE as opposed to ELECTRABASE. CNN/DM, CNN, WikiHow, XSum, Reddit) shows results using BERTBASE as the pretrained encoder. While the absolute ROUGE. Results on WikiHow, XSum, and Reddit with replacing CUPSEXT with MatchSum (Zhong et al, 2020), a state-of-the-art extractive model

samples: 500
Results on CNN, WikiHow, XSum, and Reddit with removing the plausibility model in CUPSCMP. Results on NYT → CNN, reporting ROUGE with standard deviation across 5 independent runs with a random batch of 500 samples. Results on CNN → Reddit, reporting ROUGE with standard deviation across 5 independent runs with a random batch of 500 samples

samples: 500
Results on NYT → CNN, reporting ROUGE with standard deviation across 5 independent runs with a random batch of 500 samples. Results on CNN → Reddit, reporting ROUGE with standard deviation across 5 independent runs with a random batch of 500 samples. Results on XSum → WikiHow, reporting ROUGE with standard deviation across 5 independent runs with a random batch of 500 samples

samples: 500
Results on CNN → Reddit, reporting ROUGE with standard deviation across 5 independent runs with a random batch of 500 samples. Results on XSum → WikiHow, reporting ROUGE with standard deviation across 5 independent runs with a random batch of 500 samples. Results on NYT → CNN, CNN → Reddit, and XSum → WikiHow after removing the salience model

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