SlowGuess/NAACL_2022_OCR huggingface dataset:
Haopeng Zhang $^{1}$ , Semih Yavuz $^{2}$ , Wojciech Kryscinsk $^{2}$ , Kazuma Hashimoto $^{2}$ , and Yingbo Zhou $^{2}$
$^{1}$ IFM Lab, University of California,Davis
<sup>1</sup>haopeng@ifmlab.org
$^{2}$ $\{\text { syavuz, wojciech.kryscinski, k Hashimoto, yingbo.zhou } \}$  @salesforce.com
Abstractive summarization systems leveraging pre-training language models have achieved superior results on benchmark datasets. However, such models have been shown to be more prone to hallucinate facts that are unfaithful to the input context. In this paper, we propose a method to remedy entity-level extrinsic hallucinations with Entity Coverage Control (ECC). We first compute entity coverage precision and prepend the corresponding control code for each training example, which implicitly guides the model to recognize faithfulness contents in the training phase. We further extend our method via intermediate fine-tuning on large but noisy data extracted from Wikipedia to unlock zero-shot summarization. We show that the proposed method leads to more faithful and salient abstractive summarization in supervised fine-tuning and zero-shot settings according to our experimental results on three benchmark datasets XSum, Pubmed, and SAM-Sum of very different domains and styles.
Abstractive summarization aims to generate a compact and fluent summary that preserves the most salient content of the source document. Recent advances in pre-trained language models (Devlin et al., 2018; Liu and Lapata, 2019; Lewis et al., 2020) have led to improvements in the quality of generated summaries.
However, one prominent limitation of existing abstractive summarization systems is the lack of faithfulness of generated outputs. Faithful summaries should only contain content that can be derived from the source document instead of hallucinated or fabricated statements. Summary hallucination could be categorized by the information source as intrinsic and extrinsic hallucinations. Cao et al. (2018); Krysciński et al. (2019) showed that about  $30\%$  of the summaries generated by seq2seq models suffer from the hallucination phenomenon at
Source: When the experiments are eventually run, the results will be streamed live on YouTube. Alongside Prof Hawking, the judging panel consists of [...]
Summary: Stephen Hawking joined the judging panel of a science competition on the internet education site Gumtree.
Table 1: An example of model generated unfaithful summary due to entity hallucination from XSum dataset.
either the entity level or the summary level. Table 1 shows an example of a model generated summary with hallucinated entities. The BBC article discusses a teenage science competition streamed on the Youtube website, while a BART-based summarizer makes up the term 'Gumtree' instead. Such hallucinations may cause factual errors and hinder the practical use of summarization models.
Faithfulness and factuality in abstractive summarization has received growing attention from the NLP community (Kryscinski et al., 2020; Goyal and Durrett, 2021; Zhu et al., 2021; Narayan et al., 2021). Recent works have attempted to address the hallucination problem at the entity level by reducing hallucinated entities during generation. Chen et al. (2021) proposed a post-processing method, which replaces the hallucinated entities in the generated outputs with the same type entities in the source document. However, it introduces additional errors to the summary and increases the intrinsic hallucination. Nan et al. (2021) proposed to address entity hallucination by filtering the training data and multi-task learning with summary-worthy named-entities classification. However, the method sacrifices part of the training data and decreases the quality of the summary.
To address the above issues, we propose to solve entity hallucination by guiding the model learning process with entity control code (Ecc) (Keskar et al., 2019; He et al., 2020; Fan et al., 2017). We utilize the entity coverage precision between the training document and its reference summary as faithfulness guidance andptides it to the corre
Findings of the Association for Computational Linguistics: NAACL 2022, pages 528 - 535
Figure 1: Entity Coverage Control for seq2seq model.
sponding document in the training phase. Then, we prepend faithful control code during inference and reduce hallucinated entities effectively without decreasing the fluency and salience of generated summaries according to our experimental results. In addition, we extend control code to a Wikipedia-based intermediate fine-tuning model, which generates faithful and salient summaries across domains in the zero-shot setting. We validate our methods on three benchmark datasets across different domains, and experimental results demonstrate the effectiveness of our methods.
Let  $D = \{(d_1, s_1), (d_2, s_2), \ldots, (d_n, s_n)\}$  denote a dataset composed of  $n$  document and summary pairs. During inference phase, a seq2seq model generates summary hypothesis  $h_i$  for a given document  $d_i$  by computing the probability  $p_{\theta}(h_i | d_i)$ . The generated summary  $h_i$  is expected to be faithful, which means all the information in  $h_i$  should be entailed by the source document  $d_i$ .
Following (Nan et al., 2021), we quantify entity-level hallucination with entity coverage precision precen. It approximates the faithfulness by measuring the ratio of the named entities in the summary that are coming from the source document. Formally, it is defined as:
$$
\operatorname {p r e c} _ {\mathrm {e n}} = \left| \mathcal {N} (h) \cap \mathcal {N} (s) \right| / \left| \mathcal {N} (h) \right| \tag {1}
$$
where  $\mathcal{N}(t)$  represents the set of all named entities found in a given input text  $t$ .
2.2 Entity Coverage Control
Figure 1 shows our entity coverage control method. We generate a control code  $C_i$  for each training document and reference summary pair  $(d_i, s_i)$  so the seq2seq model generates a summary conditioned on both the source document  $d_i$  and its control code  $C_i$ , which is represented as  $p_{\theta}(h_i | d_i, C_i)$ .
We first compute entity coverage precision  $\mathbf{prec}_{\mathrm{en}}$  for each document and reference summary pair  $(d_i, s_i)$  in the training set  $D$ . Then, we quantize  $\mathbf{prec}_{\mathrm{en}}$  into  $k$  discrete bins, each representing a range of entity faithfulness. These bin boundaries are selected to ensure that each bin contains roughly the same number of training examples to avoid data imbalance. We then represent each bin by a special token control code  $C_i$  and add all these special tokens  $\{C_1, C_2, \dots, C_k\}$  to the input vocabulary of our seq2seq model.
During training, we prepend the corresponding pseudo label  $C_i$  to the input document as control code. The seq2seq model is now conditioned on both the source document  $d_i$  and its control code  $C_i$ , so it could learn different faithful level generation patterns from the control codes. Then during inference, we prepend the high faithfulness control code  $C_k$  to all documents in the test set and generate faithful summaries by  $p_{\theta}(h_i|d_i,C_k)$ .
2.3 Controllable Intermediate Fine-tuning
Large pre-trained language models (Devlin et al., 2018; Lewis et al., 2019) perform poorly in the zero-shot summarization setting since sentence salience information is not learned through pretraining tasks (Zhang et al., 2020b). Thus, we propose a controllable generalized intermediate finetuning for zero-shot summarization.
We first generate pseudo document summary pairs from Wikipedia article dump with similar summary length  $(n)$ , document length  $(m)$  and abstractiveness  $(a)$  to the target datasets following Wikitransfer (Fabbri et al., 2021). Instead of training different models for different target datasets as in WikiTransfer, we propose a unified model that generalizes well across different domains. Assume we have  $l$  target-specific pseudo training subsets  $\{D_{1}(n_{1},m_{1},a_{1}),\dots,D_{l}(n_{l},m_{l},a_{l})\}$ , we give each subset another special token  $E_{i}$  as a pseudo label to represent the target-specific pattern and also add all these special tokens  $\{E_1,E_2,\dots,E_l\}$  to the input vocabulary of the seq2seq model. In the training phase, we prepend the corresponding target code  $E_{i}$  to the document, and a summary is generated conditioned on both the source document  $d_{i}$  and its target control code  $E_{i}$ , which is represented as  $p_{\theta}(h_i|d_i,E_i)$ . This allows for control over the domain and generation style of generated summaries by prepending different domain control codes during inference. The control codes
Pubmed
Model	Entity Precision	R-1	R-2	R-L
Reference	42.85	100	100	100
BARTlarge	74.31	43.35	16.20	39.50
ECC	76.38	43.46	16.24	39.68
SAMSum
Model	Entity Precision	R-1	R-2	R-L
Reference	71.20	100	100	100
BARTlarge	78.50	52.39	27.89	43.58
Ecc	80.23	52.42	27.69	43.34
Table 2: Experiment results in the supervised fine-tuning setting on Pubmed and SAMsum datasets, XSum results are reported in Table 3
XSum
Model	Entity Precision	FEQA	R-1	R-L
BART	54.11	22.50	44.78	36.64
+Correction	55.57	25.62	43.48	35.32
+Filter	70.49	26.73	42.19	33.97
ECC	59.38	26.51	43.82	35.97
Table 3: Performance comparison against state-of-the-art baselines on XSum dataset.
are also stackable, so we can stack the target control with entity coverage control for faithful zero-shot summarization, which could be denoted as  $p_{\theta}(h_i|d_i,C_i,E_i)$ .
Datasets, evaluation and implementation: We experiment with three summarization datasets in different domains: news dataset XSum (Narayan et al., 2018), scientific paper dataset Pubmed (Cohen et al., 2018), and dialogue dataset Samsum (Gliwa et al., 2019). We use Entity Precision (Nan et al., 2021) and FEQA (Durmus et al., 2020) to evaluate summary faithfulness and use ROUGE (Lin, 2004) to evaluate the fluency and salience. We also ask expert annotators to perform a human evaluation in both summary faithfulness and quality. We use BART-large as backbone model and set hyperparameter  $k = 3$  for all experiments. The three discrete ECC bins are represented with control codes: <FF-low>, <FF-mid> and <FF-high> respectively. More implementation details are described in Appendix A.
Baselines: We compare our methods with two state-of-the-art methods in summarization faithfulness: (1)Post-processing correction in (Chen et al., 2021) (2)Entity-based data filtering in (Nan et al.,
Xsum
Model	Entity Precision	R-1	R-2	R-L
BART	92.61	19.45	3.01	13.29
WIKITRANSFER	50.50	29.39	8.90	21.98
ECC-zero	55.48	30.05	9.72	22.99
Pubmed
Model	Entity Precision	R-1	R-2	R-L
BART	42.85	31.65	10.17	16.60
WIKITRANSFER	62.72	38.64	13.28	19.37
ECC-zero	68.13	38.42	13.34	19.32
Table 4: Model performance in the zero-shot summarization setting.
Model	Faith. %	Ex. %	In. %	Quality
BART	15.0	54.0	39.0	2.31
+Correction	27.0	48.0	57.0	2.42
ECC	28.0	41.0	37.0	2.43
ECC-zero	31.0	48.0	38.0	1.73
Table 5: Human evaluation results of 50 test examples sampled from XSum dataset. Results with inter-annotator agreement are reported in Appendix C.
2021) together with original BART. For zero-shot summarization, we compare with state-of-the-art method WikiTransfer (Fabbri et al., 2021).
3.2 Automatic Evaluation
Table 2 shows the performance of ECC in the supervised setting. Compared to the summaries generated by BART, our method increases the entity coverage precision significantly with roughly the same summary quality. Table 3 shows the performance comparison to strong baselines on the XSum dataset. Our methods achieve comparable faithfulness improvements without degrading the summary quality compared to data filtering and post-processing methods. We notice there is a trade-off between entity coverage precision and summary quality in Xsum dataset, which is likely due to the low faithfulness level of the reference summaries of Xsum (Maynez et al., 2020).
Table 4 shows the zero-shot summarization results. We notice BART tends to copy from the source document, so it achieves high entity coverage precision (92.61) but low summary quality. In contrast, with our intermediate fine-tuning, BART learns the characteristic of the downstream dataset and achieves a considerable improvement in ROUGE score. Compared to the baseline Wikitransfer, we see improvements in both the entity coverage precision and summary quality. Our model is also generalized cross datasets, so we use
Figure 2: Number of entities in the generated summary from BART and ECC.
Model	Entity Precision	R-1	R-2	R-L
BARTlarge	54.11	44.78	21.60	36.64
LOW	51.32	44.03	21.23	36.12
MEDIUM	53.50	43.94	21.21	35.94
HIGH	59.38	43.82	21.15	35.97
Table 6: Comparison of summaries decoding with different control codes on XSum Dataset.
one model for different downstream targets instead of training separate models like Wikitransfer.
Table 5 shows the human evaluation results on the 50 randomly sampled subset of articles from the XSum dataset following the setting of (Chen et al., 2021). Four expert annotators assign each summary output into three faithfulness categories (faithful summary, intrinsic hallucination, extrinsic hallucination) and three summary quality categories (low(1), medium (2), high(3)). Note that a summary may contain both intrinsic and extrinsic hallucinations. As the results show, our ECC model improves the faithfulness of the summaries without degrading summary quality, which agrees with our automatic evaluation results.
Document: Saints captain <mask> Anderson claims he was punched by Kiernan during last week's 1-1 draw between the sides. [...] Bart: St Johnstone's Gary Anderson says Rangers midfielder John Kiernan should face a Scottish FA disciplinary hearing over an alleged punch. Reconstructed <mask> from 1st sentence context: Top-5: ['Paul', 'Mark', 'Tom', 'James', 'Ryan'] Reconstructed <mask> from full source context: Top-5: ['Craig', 'Gary', 'Kier', 'Steven', 'Anderson']
Table 7: An example of hallucinated entity analysis with mask token refilling by BART. The ground truth is 'Steven Anderson' according to web search.
Does our model generate fewer entities to be safe? One obvious way to get higher entity coverage precision is to avoid generating entities or generating extra non-sense named entities from the source document. We show the distribution of the number of entities in the generated summaries by our model and BART in Fig 2. We see that the two distributions are very similar and have almost the same mean number of entities. As a result, we argue that our method doesn't under-generate nor over-generate entities from the source document, and we don't need to separately control the entity compression rate.
How does control code affect inference phase? We also study the effect of decoding with different control codes. We pretend different entity coverage control codes during inference on the XSum test set. As shown in Table 6, our model still generates reasonable summaries when inferred with low and medium control codes. We notice that summaries inferred with low control codes have higher ROUGE scores, which agrees with the trade-off described earlier.
Why does BART generate hallucinated tokens? As shown in Table 7, fine-tuned BART generates 'Gary Anderson' according to the context 'Saints captain Anderson', which is erroneous since the actual captain is 'Steven Anderson'. Language models contain abundant relational knowledge from pre-training data and could be extracted by masked text filling (Petroni et al., 2019). Similarly, we insert a mask token before 'Anderson' and probe untuned BART to fill the masked tokens. BART generates 'Paul Anderson' (actor) when only given the first sentence context. When given the whole news article, BART learns the context is sports-related and generates famous athletes 'Craig Anderson' (hockey athlete) and 'Gary Anderson' (football athlete) according to its pre-trained prior knowledge. The ground truth 'Steven Anderson' appears much less frequent during pre-training, so BART has a low probability of generating it correctly. We observe the same for ground truth 'Rob Kiernan', which probably appears less frequently in BART's pre-training corpus.
The faithfulness and factuality in abstractive summarization has received growing attention by the summarization community recently (Kryscinski et al., 2020; Cao et al., 2018; Goyal and Durrett, 2021). Maynez et al. (2020) categorized hallucinations by the information source as intrinsic and extrinsic hallucinations. Researchers have turned to textual entailment (Maynez et al., 2020), question answering (QA)(Durmus et al., 2020; Wang et al., 2020), Natural Language Inference (NLI) (Kryscinski et al., 2020) and entity level precision (Nan et al., 2021) for automatic faithfulness evaluation. To improve the faithfulness of generated summaries. Cao et al. (2018) proposes a fact-aware summarization model with open information extraction and dependency parse technologies. (Zhu et al., 2021) uses graph attention to integrate factual relations into the summary generation process. Recent works also focus on addressing entity-level hallucination problems. Chen et al. (2021) proposes a post-processing method to correct hallucinated entities and (Nan et al., 2021) addresses entity hallucination by filtering the training data and multi-task learning. One concurrent work Narayan et al. (2021) incorporates entity chain content planning to guide faithful summary generation.
The transformer-based seq2seq architecture (Vaswani et al., 2017) currently dominates the state-of-the-art performance in many NLP tasks (Liu and Lapata, 2019; Zhang et al., 2020a; Zhang and Zhang, 2020). We use BART (Lewis et al., 2020) as a backbone for abstractive summarization in this work, but our method is generally applicable for all seq2seq models.
Our work is also related to controllable abstractive summarization. Liu et al. (2018) controls the summary length by extending a convolutional sequence to sequence model. He et al. (2020) introduces a keyword guided framework for entity-centric, length-controllable summarization and question-guided summarization. Fan et al. (2017) proposes to control the summary generation with a list of desired named entities. Recently, Feng et al. (2021) proposes to use language models to generate pseudo labels to control the generation of dialogue summarization. Our work uses control code to improve summary generation faithfulness and cross-domain generalizability.
In this paper, we propose ECC to address extrinsic hallucination in abstractive summarization in both supervised and zero-shot settings. Our extensive experiments demonstrate that the proposed method effectively reduces entity hallucination without hurting the quality of the generated summaries.
Work done during internship at Salesforce Research. Thanks to Man Luo, Xi Ye, and everyone at Salesforce Research for helpful discussions, as well as to the anonymous reviewers for their helpful feedback.
Ziqiang Cao, Furu Wei, Wenjie Li, and Sujian Li. 2018. Faithful to the original: Fact aware neural abstractive summarization. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 32.
Sihao Chen, Fan Zhang, Kazoo Sone, and Dan Roth. 2021. Improving faithfulness in abstractive summarization with contrast candidate generation and selection. arXiv preprint arXiv:2104.09061.
Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, and Nazli Goharian. 2018. A discourse-aware attention model for abstractive summarization of long documents. arXiv preprint arXiv:1804.05685.
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.
Esin Durmus, He He, and Mona Diab. 2020. FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5055-5070, Online. Association for Computational Linguistics.
Alexander Fabbri, Simeng Han, Haoyuan Li, Haoran Li, Marjan Ghazvininejad, Shafiq Joty, Dragomir Radev, and Yashar Mehdad. 2021. Improving zero and few-shot abstractive summarization with intermediate fine-tuning and data augmentation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 704-717, Online. Association for Computational Linguistics.
Angela Fan, David Grangier, and Michael Auli. 2017. Controllable abstractive summarization. arXiv preprint arXiv:1711.05217.
Xiaocheng Feng, Libo Qin, Bing Qin, and Ting Liu. 2021. Language model as an annotator: Exploring dialogpt for dialogue summarization. arXiv preprint arXiv:2105.12544.
Bogdan Gliwa, Iwona Mochol, Maciej Biesek, and Aleksander Wawer. 2019. Samsum corpus: A human-annotated dialogue dataset for abstractive summarization. arXiv preprint arXiv:1911.12237.
Tanya Goyal and Greg Durrett. 2021. Annotating and modeling fine-grained factuality in summarization. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1449-1462, Online. Association for Computational Linguistics.
Junxian He, Wojciech Krysciński, Bryan McCann, Nazneen Rajani, and Caiming Xiong. 2020. Ctrlsum: Towards generic controllable text summarization. arXiv preprint arXiv:2012.04281.
Nitish Shirish Keskar, Bryan McCann, Lav R Varshney, Caiming Xiong, and Richard Socher. 2019. Ctrl: A conditional transformer language model for controllable generation. arXiv preprint arXiv:1909.05858.
Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
Wojciech Krysciński, Nitish Shirish Keskar, Bryan McCann, Caiming Xiong, and Richard Socher. 2019. Neural text summarization: A critical evaluation. arXiv preprint arXiv:1908.08960.
Wojciech Kryscinski, Bryan McCann, Caiming Xiong, and Richard Socher. 2020. Evaluating the factual consistency of abstractive text summarization. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 9332-9346, Online. Association for Computational Linguistics.
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461.
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020. BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7871-7880, Online. Association for Computational Linguistics.
Chin-Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out, pages 74-81.