SlowGuess/NAACL_2021_OCR huggingface dataset:
Chenguang Zhu<sup>1</sup>, William Hinthorn<sup>1</sup>, Ruochen Xu<sup>1</sup>, Qingkai Zeng<sup>2</sup>, Michael Zeng<sup>1</sup>, Xuedong Huang<sup>1</sup>, Meng Jiang<sup>2</sup>
<sup>1</sup> Microsoft Cognitive Services Group
2 University of Notre Dame
{chezhu, wihintho, ruox, nzeng, xdh} @microsoft.com
{qzeng,mjiang2}@nd.edu
Automatic abstractive summaries are found to often distort or fabricate facts in the article. This inconsistency between summary and original text has seriously impacted its applicability. We propose a fact-aware summarization model FASUM to extract and integrate factual relations into the summary generation process via graph attention. We then design a factual corrector model FC to automatically correct factual errors from summaries generated by existing systems. Empirical results show that the fact-aware summarization can produce abstractive summaries with higher factual consistency compared with existing systems, and the correction model improves the factual consistency of given summaries via modifying only a few keywords.
Text summarization models aim to produce an abridged version of long text while preserving salient information. Abstractive summarization is a type of such models that can freely generate summaries, with no constraint on the words or phrases used. This format is closer to human-edited summaries and is both flexible and informative. Thus, there are numerous approaches to produce abstractive summaries (See et al., 2017; Paulus et al., 2017; Dong et al., 2019; Gehrmann et al., 2018).
However, one prominent issue with abstractive summarization is factual inconsistency. It refers to the hallucination phenomenon that the summary sometimes distorts or fabricates the facts in the article. Recent studies show that up to  $30\%$  of the summaries generated by abstractive models contain such factual inconsistencies (Krysciński et al., 2019b; Falke et al., 2019), raising concerns about the credibility and usability of these systems.
Article	Real Madrid ace Gareth Bale treated himself to a Sunday evening BBQ...
The Welsh wizard was ... scoring twice and assisting another in an impressive victory...
Cristiano Ronaldo scored five goals against Granada on Sunday ...
BOTTOMUP	... The Real Madrid ace scored five goals against Granada on Sunday. The Welsh wizard was in impressive form for...
SEQ2SEQ	...Gareth Bale scored five and assisted another in an impressive win in Israel...
FASUM (Ours)	...Gareth Bale scored twice and helped his side to a sensational 9-1 win. Cristiano Ronaldo scored five goals against Granada on Sunday...
Table 1: Example article and summary excerpts from CNN/DailyMail dataset.
Table 1 demonstrates an example article and excerpts of generated summaries. As shown, the article mentions that Real Madrid ace Gareth Bale scored twice and Cristiano Ronaldo scored five goals. However, both BOTTOMUP (Gehrmann et al., 2018) and SEQ2SEQ wrongly states that Bale scored five goals. Comparatively, our model FASUM generates a summary that correctly exhibits the fact in the article. And as shown in Section 4.6.1, our model achieves higher factual consistency not just by making more copies from the article.
On the other hand, most existing abstractive summarization models apply a conditional language model to focus on the token-level accuracy of summaries, while neglecting semantic-level consistency between the summary and article. Therefore, the generated summaries are often high in token-level metrics like ROUGE (Lin, 2004) but lack factual consistency. In view of this, we argue that a robust abstractive summarization system must be equipped with factual knowledge to accurately summarize the article.
In this paper, we represent facts in the form of knowledge graphs. Although there are numerous
1We provide the prediction results of all models at https: //github.com/zcgzcgzcg1/FASum/.
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 718-733 June 6-11, 2021. ©2021 Association for Computational Linguistics
Article	The flame of remembrance burns in Jerusalem, and a song of memory haunts Valerie Graham as it never has before. This year, *Israel's Memorial Day* commemoration is for bereaved family members such as Graham. "Now I truly understand everyone who has lost a loved one," Graham said. Her husband, Philippe Graham, was one of 17 people killed in January's terror attacks in Paris...
As Israel mourns on the nation's remembrance day, French Prime Minister Manuel Valls announced after his weekly Cabinet meeting that French authorities had foiled a terror plot...
BOTTOMUP	Valerie Graham was one of 17 people killed in January's terror attacks in Paris. France's memorial day commemoration is for bereaved family members as Graham. *Israel's Prime Minister* says the terror plot has not been done.
Corrected by FC	Philippe Graham was one of 17 people killed in January's terror attacks in Paris. *Israel's memorial day* commemoration is for bereaved family members as Graham. France's Prime Minister says the terror plot has not been done.
Table 2: Example excerpts of an article from CNN/DailyMail and the summary generated by BOTTOMUP. Factual errors are marked in red. The correction made by our model FC are marked in green.
efforts in building commonly applicable knowledge graphs such as ConceptNet (Speer et al., 2017), we find that these tools are more useful in conferring commonsense knowledge. In abstractive summarization for contents like news articles, many entities and relations are previously unseen. Plus, our goal is to produce summaries that do not conflict with the facts in the article. Thus, we propose to extract factual knowledge from the article itself.
We employ the information extraction (IE) tool OpenIE (Angeli et al., 2015) to extract facts from the article in the form of relational tuples: (subject, relation, object). This graph contains the facts in the article and is integrated in the summary generation process.
Then, we use a graph attention network (Velicković et al., 2017) to obtain the representation of each node, and fuse that into a transformer-based encoder-decoder architecture via attention. We denote this model as the Fact-Aware Summarization model, FASUM.
In addition, to be generally applicable for all existing summarization systems, we propose a Factual Corrector model, FC, to help improve the factual consistency of any given summary. We frame the correction process as a seq2seq problem: the input is the original summary and the article, and the output is the corrected summary. FC has the same architecture as UniLM (Dong et al., 2019) and initialized with weights from RoBERTa-Large (Liu et al., 2019). We finetune it as a denoising autoencoder. The training data is synthetically generated via randomly replacing entities in the ground-truth summary with wrong ones in the article. As shown in Table 2, FC makes three corrections, replacing the original wrong entities which appear elsewhere in the article with the right ones.
In the experiments, we leverage an independ-
dently trained BERT-based (Devlin et al., 2018) factual consistency evaluator (KrySciński et al., 2019b). Results show that on CNN/DailyMail, FA-SUM obtains  $0.6\%$  higher fact consistency scores than UNILM (Dong et al., 2019) and  $3.9\%$  higher than BOTTOMUP (Gehrmann et al., 2018). Moreover, after correction by FC, the factual score of summaries from BOTTOMUP increases  $1.4\%$  on CNN/DailyMail and  $0.9\%$  on XSum, and the score of summaries from TCONVS2S increases  $3.1\%$  on XSum. We also conduct human evaluation to verify the effectiveness of our models.
We further propose an easy-to-compute model-free metric, relation matching rate (RMR), to evaluate factual consistency given a summary and the article. This metric employs the extracted relations and does not require human-labelled summaries. Under this metric, we show that our models can help enhance the factual consistency of summaries.
Abstractive text summarization has been intensively studied in recent literature. Rush et al. (2015) introduces an attention-based seq2seq model for abstractive sentence summarization. See et al. (2017) uses copy-generate mechanism that can both produce words from the vocabulary via a generator and copy words from the article via a pointer. Paulus et al. (2017) leverages reinforcement learning to improve summarization quality. Gehrmann et al. (2018) uses a content selector to over-determine phrases in source documents that helps constrain the model to likely phrases. Zhu et al. (2019) defines a pretraining scheme for summarization and produces a zero-shot abstractive summarization model. Dong et al. (2019) employs different masking techniques for both NLU and NLG tasks,
resulting in the UNILM model. Lewis et al. (2019) employs denoising techniques to help generation tasks including summarization.
2.2 Fact-Aware Summarization
Entailment models have been used to evaluate and enhance factual consistency of summarization. Li et al. (2018) co-trains summarization and entailment and employs an entailment-aware decoder. Falke et al. (2019) proposes using off-the-shelf entailment models to rerank candidate summary sentences to boost factual consistency.
Zhang et al. (2019b) employs descriptor vectors to improve factual consistency in medical summarization. Cao et al. (2018) extracts relational information from the article and maps it to a sequence as an additional input to the encoder. Gunel et al. (2019) employs an entity-aware transformer structure for knowledge integration, and Matsumaru et al. (2020) improves factual consistency of generated headlines by filtering out training data with more factual errors. In comparison, our model utilizes the knowledge graph extracted from the article and fuses it into the generated text via neural graph computation.
To correct factual errors, Dong et al. (2020) uses pre-trained NLU models to rectify one or more wrong entities in the summary. Concurrent to our work, Cao et al. (2020) employs the generation model BART (Lewis et al., 2019) to produce corrected summaries.
Several approaches have been proposed to evaluate a summary's factual consistency (Krysciński et al., 2019a; Goodrich et al., 2019; Maynez et al., 2020). Zhang et al. (2019a) employs BERT to compute similarity between pairs of words in the summary and article. Wang et al. (2020); Durmus et al. (2020) use question answering accuracy to measure factual consistency. Krysciński et al. (2019b) applies various transformations on the summary to produce training data for a BERT-based classification model, FactCC, which shows a high correlation with human metrics. Therefore, we use FactCC as the factual evaluator in this paper.
We formalize abstractive summarization as a supervised seq2seq problem. The input consists of  $a$  pairs of articles and summaries:  $\{(X_1,Y_1),(X_2,Y_2),\ldots ,(X_a,Y_a)\}$ . Each article
is tokenized into  $X_{i} = (x_{1},\dots,x_{L_{i}})$  and each summary is tokenized into  $Y_{i} = (y_{1},\dots,y_{N_{i}})$ . In abstrative summarization, the model-generated summary can contain tokens, phrases and sentences not present in the article. For simplicity, in the following we will drop the data index subscript. Therefore, each training pair becomes  $X = (x_{1},\dots,x_{m}),Y = (y_{1},\dots,y_{n})$ , and the model needs to generate an abstrative summary  $\hat{Y} = (\hat{y}_1,\dots,\hat{y}_{n'})$ .
3.2 Fact-Aware Summarizer
We propose the Fact-Aware abstractive Summarizer, FASUM. It utilizes the seq2seq architecture built upon transformers (Vaswani et al., 2017). In detail, the encoder produces contextualized embeddings of the article and the decoder attends to the encoder's output to generate the summary.
To make the summarization model fact-aware, we extract, represent and integrate knowledge from the source article into the summary generation process, which is described in the following. The overall architecture of FASUM is shown in Figure 1.
3.2.1 Knowledge Extraction
To extract important entity-relation information from the article, we employ the Stanford OpenIE tool (Angeli et al., 2015). The extracted knowledge is a list of tuples. Each tuple contains a subject (S), a relation (R) and an object (O), each as a segment of text from the article. In the experiments, there are on average 165.4 tuples extracted per article in CNN/DailyMail (Hermann et al., 2015) and 84.5 tuples in XSum (Narayan et al., 2018).
3.2.2 Knowledge Representation
We construct a knowledge graph to represent the information extracted from OpenIE. We apply the Levi transformation (Levi, 1942) to treat each entity and relation equally. In detail, suppose a tuple is  $(s,r,o)$ , we create nodes  $s$ ,  $r$  and  $o$ , and add edges  $s—r$  and  $r—o$ . In this way, we obtain an undirected knowledge graph  $G = (V,E)$ , where each node  $v\in V$  is associated with text  $t(v)$ . During training, this graph G is constructed for each batch individually, i.e. there's no shared huge graph. One benefit is that the model can take unseen entities and relations during inference.
We then employ a graph attention network (Veličković et al., 2017) to obtain embedding  $e_j$  for each node  $v_j$ . The initial embedding of  $v_j$  is given by the last hidden state of a bidirectional LSTM
Figure 1: The model architecture of FASUM. It has  $L$  layers of transformer blocks in both the encoder and decoder. The knowledge graph is obtained from information extraction results and it participates in the decoder's attention.
applied to  $t(v_{j})$ . In the experiment, we employ 2 graph attention layers.
3.2.3 Knowledge Integration
The knowledge graph embedding is obtained in parallel with the encoder. Then, apart from the canonical cross attention over the encoder's outputs, each decoder block also computes cross-attention over the knowledge graph nodes' embeddings:
$$
\alpha_ {i j} = \operatorname {s o f t m a x} _ {j} \left(\beta_ {i j}\right) = \frac {\exp \left(\beta_ {i j}\right)}{\sum_ {j \in V} \exp \left(\beta_ {i j}\right)} \tag {1}
$$
$$
\beta_ {i j} = \boldsymbol {s} _ {i} ^ {T} \boldsymbol {e} _ {j}, \tag {2}
$$
$$
\boldsymbol {u} _ {i} = \sum_ {j \in V} \alpha_ {i j} \boldsymbol {e} _ {j}, \tag {3}
$$
where  $\{e_j\}_{j = 1}^{|V|}$  are the final embeddings of the graph nodes, and  $\{s_i\}_{i = 1}^t$  are the decoder block's representation of the first  $t$  generated tokens.
3.2.4 Summary Generation
We denote the final output of the decoder as  $z_{1}, \ldots, z_{t}$ . To produce the next token  $y_{t+1}$ , we employ a linear layer  $W$  to project  $z_{t}$  to a vector of the same size of the dictionary. And the predicted distribution of  $y_{t+1}$  is obtained by:
$$
\boldsymbol {p} ^ {t + 1} = \sigma (\boldsymbol {W} \boldsymbol {z} _ {t}) \tag {4}
$$
During training, we use cross entropy as the loss function  $\mathcal{L}(\theta) = -\sum_{t=1}^{n} \boldsymbol{y}_t^T \log(\boldsymbol{p}^t)$ , where  $\boldsymbol{y}_t$  is the one-hot vector for the  $t$ -th token, and  $\theta$  represents the parameters in the network.
3.3 Fact Corrector
To better utilize existing summarization systems, we propose a Factual Corrector model, FC, to improve the factual consistency of any summary generated by abstractive systems. FC frames the correction process as a seq2seq problem: given an article and a candidate summary, the model generates a corrected summary with minimal changes to be more factually consistent with the article.
While FASum has a graph attention module in the transformer, preventing direct adaptation from pre-trained models, the FC model architecture adopts the design of the pre-trained model UniLM (Dong et al., 2019). We initialized the model weights from RoBERTa-Large (Liu et al., 2019). The finetuning process is similar to training a denoising autoencoder. We use back-translation and entity swap for synthetic data generation. For example, an entity in the ground-truth summary is randomly replaced with another entity of the same type from the article. This modified summary and the article is sent to the corrector to recover the original summary. In the experiments, we gener
ated 3.0M seq2seq data samples in CNN/DailyMail and 551.0K samples in XSum for finetuning. We take 10K samples in each dataset for validation and use the rest for training.
During inference, the candidate summary from any abstractive summarization system is concatenated with the article and sent to FC, which produces the corrected summary.
We evaluate our model on benchmark summarization datasets CNN/DailyMail (Hermann et al., 2015) and XSum (Narayan et al., 2018). They contain 312K and 227K news articles and human-edited summaries respectively, covering different topics and various summarization styles.
We use the Huggingface's (Wolf et al., 2019) implementation of transformer in BART (Lewis et al., 2019). We also inherit their provided hyperparameters of CNN/DailyMail and XSum for the beam search. The minimum summary length is 56 and 11 for CNN/Daily Mail and XSum, respectively. The number of beams is 4 for CNN/DailyMail and 6 for XSum.
In FASUM, both the encoder and decoder has 10 layers of 10 heads for attention. Teacher forcing is used in training. We use Adam (Kingma and Ba, 2014) as the optimizer with a learning rate of 2e-4.
The bi-LSTM to produce the initial embedding of graph nodes has a hidden state of size 64 and the graph attention network (GAT) has 8 heads and a hidden state of size 50. The dropout rate is 0.6 in GAT and 0.1 elsewhere.
We use the subword tokenizer SentencePiece (Kudo and Richardson, 2018). The dictionary is shared across all the datasets. The vocabulary has a size of  $32\mathrm{K}$  and a dimension of 720.
The correction model FC follows the UniLM (Dong et al., 2019) architecture initialized with weights from RoBERTa-Large (Liu et al., 2019). We fine-tune the model for 5 epochs with a learning rate of 1e-5 and linear warmup over the one-fifths of total steps and linear decay. During decoding, it uses beam search with a width of 2, and blocks triogram duplicates. The batch size during finetuning is 24. More details are presented in the Appendix.
To evaluate factual consistency, we re-implemented and trained the FactCC model (Krysciński et al., 2019b). The model outputs a score between 0 and 1, where a higher score indicates better consistency between the input article and summary. The training of FactCC is independent of our summarizer so no parameters are shared. More details are in the Appendix.
We also employ the standard ROUGE-1, ROUGE-2 and ROUGE-L metrics (Lin, 2004) to measure summary qualities. These three metrics evaluate the accuracy on unigrams, bigrams and the longest common subsequence. We report the F1 ROUGE scores in all experiments. And the ROUGE-L score on validation set is used to pick the best model for both FASUM and FC.
The following abstractive summarization models are selected as baseline systems. TCONVS2S (Narayan et al., 2018) is based on topic modeling and convolutional neural networks. BOTTOMUP (Gehrmann et al., 2018) uses a bottom-up approach to generate summarization. UNILM (Dong et al., 2019) utilizes large-scale pretraining to produce state-of-the-art abstractive summaries. We train the baseline models when the predictions are not available in their open-source repositories.
As shown in Table 3, our model  $\text{FASUM}^2$  outperforms all baseline systems in factual consistency scores in CNN/DailyMail and is only behind UNILM in XSum. In CNN/DailyMail, FASUM is  $0.6\%$  higher than UNILM and  $3.9\%$  higher than BOTTOMUP in factual score. Statistical test shows that the lead is statistically significant with p-value smaller than 0.05. The higher factual score of UNILM among baselines corroborates the findings in Maynez et al. (2020) that pre-trained models exhibit better factuality. But our proposed knowledge graph component can help the train-from-scratch FASUM model to excel in factual consistency.
We conduct ablation study to remove the knowledge graph component from FASUM, resulting in the SEQ2SEQ model. As shown, there is a clear drop in factual score:  $2.8\%$  in CNN/DailyMail and  $0.9\%$  in XSum. This proves that the constructed
2We have put code and all the generated summaries of all models in the supplementary materials.
knowledge graph can help increase the factual correctness of the generated summaries.
It's worth noticing that the ROUGE metric does not always reflect the factual consistency, sometimes even showing an inverse relationship, a phenomenon observed in multiple studies (Krysciński et al., 2019a; Maynez et al., 2020). For instance, although BOTTOMUP has 0.69 higher ROUGE-1 points than FASUM in CNN/DailyMail, there are many factual errors in its summaries, as shown in the human evaluation. On the other hand, to make sure the improved factual correctness of our models is not achieved by simply copying insignificant information from the article, we conduct analysis on abstractiveness in Section 4.6.1 and human evaluation in Section 4.6.3.
Furthermore, the correction model FC can effectively enhance the factual consistency of summaries generated by various baseline models, especially when the original summary has a relatively low factual consistency. For instance, on CNN/DM, the factual score of BOTTOMUP increases by  $1.4\%$  after correction. On XSum, after correction, the factual scores increase by  $0.2\%$  to  $3.1\%$  for all baseline models. Interestingly, FC can also boost the factual consistency of our FASUM model. Furthermore, the correction has a rather small impact on the ROUGE score, and it can improve the ROUGE scores of most models in XSum dataset.
We check and find that FC only makes modest modifications necessary to the original summaries. For instance, FC modifies  $48.3\%$  of summaries generated by BOTTOM in CNN/DailyMail. These modified summaries contain very few changed tokens:  $94.4\%$  of these corrected summaries contain 3 or fewer new tokens, while the summaries have on average 48.3 tokens.
In the appendix of supplementary materials, we show several examples of summaries given by FA-SUM and corrected by FC to demonstrate the improved factual consistency of summarization.
4.6 Insights
4.6.1 Novel n-grams
It has been shown in Durmus et al. (2020) that less abstractive summaries are more factual consistent with the article. Therefore, we inspect whether our models boost factual consistency simply by copying more portions of the article.
On XSum's testset, we compute the ratio of novel n-grams in summaries that do not appear
Figure 2: Percentage of novel n-grams for summaries in XSum test set.
in the article. Figure 2 shows that FASUM achieves the closest ratio of novel n-gram compared with reference summaries, and higher than BOTTOMUP and UNILM. This demonstrates that FASUM can produce highly abstractive summaries while ensuring factual consistency.
4.6.2 Relation Matching Rate
While the factual consistency evaluator FactCC (Krysciński et al., 2019b) is based on pre-trained models, it requires finetuning on articles and labelled summaries. Furthermore, we empirically find that the performance of FactCC degrades when it is finetuned on one summary dataset and used to evaluate models on another dataset.
Therefore, in this subsection, we design an easy-to-compute model-free factual consistency metric, which can be used when ground-truth summaries are not available.
As the relational tuples in the knowledge graph capture the factual information in the text, we compute the precision of extracted tuples in the summary. In detail, suppose the set of the relational tuples in the summary is  $R_{s} = \{(s_{i},r_{i},o_{i})\}$ , and the set of the relational tuples in the article is  $R_{a}$ . Then, each tuple in  $R_{s}$  falls into one of the following three categories:
1. Correct hit (C):  $(s_i, r_i, o_i) \in R_a$ ;
2. Wrong hit (W):  $(s_i, r_i, o_i) \notin R_a$ , but  $\exists o' \neq o_i$ ,  $(s_i, r_i, o') \in R_a$ , or  $\exists s' \neq s_i$ ,  $(s', r_i, o_i) \in R_a$ ;
3. Miss (M): Otherwise.
We define two kinds of relation matching rate