Neural abstractive summarization models are flexible and can produce coherent summaries, but they are sometimes unfaithful and can be difficult to control. While previous studies attempt to provide different types of guidance to control the output and increase faithfulness, it is not clear how these strategies compare and contrast to each other. In this paper, we propose a general and extensible guided summarization framework ( GSum ) that can effectively take different kinds of external guidance as input, and we perform experiments across several different varieties. Experiments demonstrate that this model is effective, achieving state-of-the-art performance according to ROUGE on 4 popular summarization datasets when using highlighted sentences as guidance. In addition, we show that our guided model can generate more faithful summaries and demonstrate how different types of guidance generate qualitatively different summaries, lending a degree of controllability to the learned models. 1 1 Introduction Modern techniques for text summarization generally can be categorized as either extractive methods (Nallapati et al., 2017; Narayan et al., 2018b; Zhou et al., 2018), which identify the most suitable words or sentences from the input document and concatenate them to form a summary, or abstractive methods (Rush et al., 2015; Chopra et al., 2016; Nallapati et al., 2016; Paulus et al., 2018), which generate summaries freely and are able to produce novel words and sentences. Compared with extractive algorithms, abstractive algorithms are more flexible, making them more likely to produce fluent and coherent summaries. However, the unconstrained nature of abstractive summarization can also result in problems. First, it can result 1 Code is available at https://github.com/ neulab/guided_summarization . in unfaithful summaries (Kryscinski et al., 2019), containing factual errors as well as hallucinated content. Second, it can be difficult to control the content of summaries; it is hard to pick in advance which aspects of the original content an abstractive system may touch upon. To address the issues, we propose methods for guided neural abstractive summarization: methods that provide various types of guidance signals that 1) constrain the summary so that the output content will deviate less from the source document; 2) allow for controllability through provision of user-specified inputs. There have been some previous methods for guiding neural abstractive summarization models. For example, Kikuchi et al. (2016) specify the length of abstractive summaries, Li et al. (2018) provide models with keywords to prevent the model from missing key information, and Cao et al. (2018) propose models that retrieve and reference relevant summaries from the training set. While these methods have demonstrated improvements in summarization quality and controllability, each focuses on one particular type of guidance  it remains unclear which is better and whether they are complementary to each other. In this paper, we propose a general and extensible guided summarization framework that can take different kinds of external guidance as inWork Guidance Form Tokens Triples Sentences Summaries Kikuchi et al. (2016) (cid:51) (length tokens) (cid:55) (cid:55) (cid:55) Cao et al. (2018) (cid:55) (cid:55) (cid:55) (cid:51) (retrieved sums.) Li et al. (2018) (cid:51) (keywords) (cid:55) (cid:55) (cid:55) Liu et al. (2018a) (cid:55) (cid:55) (cid:51) (highlighted sents.) (cid:55) Liu et al. (2018b) (cid:51) (length tokens) (cid:55) (cid:55) (cid:55) Fan et al. (2018) (cid:51) (length, entity, style tokens) (cid:55) (cid:55) (cid:55) Zhu et al. (2020) (cid:55) (cid:51) (relations) (cid:55) (cid:55) Jin et al. (2020) (cid:55) (cid:51) (relations) (cid:55) (cid:55) Saito et al. (2020) (cid:51) (keywords) (cid:55) (cid:51) (highlighted sents.) (cid:55) Ours (cid:51) (keywords) (cid:51) (relations) (cid:51) (highlighted sents.) (cid:51) (retrieved sums.) Table 1: A comparison of different guided neural abstractive summarization models. put. Like most recent summarization models, our model is based on neural encoder-decoders, instantiated with contextualized pretrained language models, including BERT (Devlin et al., 2019) and BART (Lewis et al., 2020). With this as a strong starting point, we make modifications allowing the model to attend to both the source documents and the guidance signals when generating outputs. As shown in Figure 1, we can provide automatically extracted or user-specified guidance to the model during test time to constrain the model output. At training time, to encourage the model to pay close attention to the guidance, we propose to use an oracle to select informative guidance signals  a simple modification that nonetheless proved essential in effective learning of our guided summarization models. Using this framework, we investigate four types of guidance signals: (1) highlighted sentences in the source document, (2) keywords, (3) salient relational triples in the form of (subject, relation, object), and (4) retrieved summaries. We evaluate our methods on 6 popular summarization benchmarks. Our best model, using highlighted sentences as guidance, can achieve state-of-the-art performance on 4 out of the 6 datasets, including 1.28/0.79/1.13 ROUGE-1/2/L improvements over previous state-of-the-art model on the widely-used CNN/DM dataset. In addition, we perform in-depth analyses of different guidance signals and demonstrate that they are complementary to each other in that there is potential to aggregate their outputs together and obtain further improvements. An analysis of the results also reveals that our guided models can generate more faithful summaries and more novel words. Finally, we demonstrate that we can control the output by providing user-specified guidance signals, with different provided signals resulting in qualitatively different summaries. Neural abstractive summarization typically takes a source document x consisting of multiple sentences x 1 ,    , x | x | , runs them through an encoder to generate representations, and passes them to a decoder that outputs the summary y one target word at a time. Model parameters  are trained to maximize the conditional likelihood of the outputs in a parallel training corpus (cid:104)X , Y(cid:105) : arg max  (cid:88) (cid:104) x i , y i (cid:105)(cid:104)X , Y(cid:105) log p ( y i | x i ;  ) . Several techniques have been proposed to improve the model architecture. For example, models of copying (Gu et al., 2016; See et al., 2017; Gehrmann et al., 2018) allow words to be copied directly from the input to the output, and models of coverage discourage the model from generating repetitive words (See et al., 2017). Within this overall framework, the types of information that go into g and the method for incorporating this information into the model may vary. While there are early attempts at non-neural guided models (Owczarzak and Dang, 2010; Genest and La-palme, 2012), here we focus on neural approaches Input Embedding FeedForward Add & Norm Self Attention Add & Norm N enc  Input Embedding FeedForward Add & Norm Self Attention Add & Norm N enc Source Document GuidanceSignal shared shared shared shared FeedForward Add & Norm Self Attention Add & Norm FeedForward Add & Norm Self Attention Add & Norm Output Embedding FeedForward Add & Norm Self Attention Add & Norm N dec Add & Norm Cross Attention Add & Norm Cross Attention Output Probabilities Softmax Linear shared Figure 2: General framework of our model. and summarize recent work in Table 1. For example, Li et al. (2018) first generate a set of keywords , which are then incorporated into the generation process by an attention mechanism. Cao et al. (2018) propose to search the training corpus and retrieve datapoint (cid:104) x j , y j (cid:105) whose input document x j is most relevant to the current input x , and treat y j as a candidate template to guide the summarization process. Besides, Jin et al. (2020) and Zhu et al. (2020) extract relational triples in the form of (subject, relation, object) from source documents and represent them by graph neural networks. The decoders then attend to the extracted relations to generate faithful summaries. A concurrent work by Saito et al. (2020) propose to extract keywords or highlighted sentences using saliency models and feed them to summarization models. There are also works on controlling the summary length (Kikuchi et al., 2016; Liu et al., 2018b) and styles (Fan et al., 2018) by explicitly feeding the desired features to the model. In addition, Liu et al. (2018a) and Chen and Bansal (2018) follow a two-stage paradigm, in which a subset of the source document { x i 1 ,    , x i n } will first be selected by a pretrained extractor as highlighted sentences and then be fed into the model encoder in the second stage with the rest of the text discarded. Figure 2 illustrates the general framework of our proposed method. We feed both the source documents and various types of guidance signals to the model. Specifically, we experiment with guidance signals including highlighted sentences, keywords, relations, and retrieved summaries, although the framework is general and could be expanded to other varieties of guidance as well. We adopt the Transformer model (Vaswani et al., 2017) as our backbone architecture, instantiated with BERT or BART, which can be separated into the encoder and decoder components. Similar to the Transformer model, each of our encoders is composed of N enc + 1 layers, with each encoding layer containing both a self-attention block and a feed-forward block: x = LN ( x + SELFATTN ( x )) , x = LN ( x + FEEDFORWARD ( x )) , where LN denotes layer normalization. Note the source document and guidance signal do not interact with each other during encoding. We share the parameters of the bottom N enc layers and the word embedding layers between the two encoders, because 1) this can reduce the computation and memory requirements; 2) we conjecture that the differences between source documents and guidance signals should be high-level, which are captured at top layers of the encoders. Different from the standard Transformer, our decoder has to attend to both the source document and guidance signal instead of just one input. Concretely, our decoder is composed of N dec identical layers, with each layer containing four blocks. After the self-attention block, the decoder will first attend to the guidance signals and generate the corresponding representations, and hence the guidance signal will inform the decoder which part of the source documents should be focused on. Then, the decoder will attend to the whole source document based on the guidance-aware representations. Finally, the output representation will be fed into the feed-forward block: y = LN ( y + SELFATTN ( y )) , y = LN ( y + CROSSATTN ( y , g )) , y = LN ( y + CROSSATTN ( y , x )) , y = LN ( y + FEEDFORWARD ( y )) . Ideally, the second cross-attention block allows the model to fill in the details of the input guidance signal, such as finding the name of an entity by searching through co-reference chains. Before delving into the specifics of the types of guidance signal we used, we first note an important detail in training our model. At test time, there are two ways we can define the guidance signal: 1) manual definition where an interested user de-fines the guidance signal g by hand, and 2) automatic prediction where an automated system is used to infer the guidance signal g from input x . We demonstrate results for both in experiments. At training time, it is often prohibitively expensive to obtain manual guidance. Hence, we focus on two varieties of generating them: 1) automatic prediction using x as detailed above, and 2) oracle extraction where we use both x and y to deduce a value g that is most likely useful in generating y . Theoretically, automatic prediction has the advantage of matching the training and testing conditions of a system that will also receive automatic predictions at test time. However, as we will show in experiments, the use of oracle guidance has a large advantage of generating guidance signals that are highly informative, thus encouraging the model to pay more attention to them at test time. With this in mind, we describe the four varieties of guidance signal we experiment with, along with their automatic and oracle extraction methods. Highlighted Sentences. The success of extractive approaches have demonstrated that we can extract a subset of sentences { x i 1 ,    , x i n } from the source document and concatenate them to form a summary. Inspired by this, we explicitly inform our model which subset of source sentences should be highlighted using extractive models. We perform oracle extraction using a greedy search algorithm (Nallapati et al., 2017; Liu and Lapata, 2019) to find a set of sentences in the source document that have the highest ROUGE scores with the reference (detailed in Appendix) and treat these as our guidance g . At test time, we use pretrained extractive summarization models (BertExt (Liu and Lapata, 2019) or MatchSum (Zhong et al., 2020) in our experiments) to perform automatic prediction. occur in an actual summary, which could distract the model from focusing on the desired aspects of the input. Therefore, we also try to feed our model with a set of individual keywords { w 1 , . . . , w n } from the source document. For oracle extraction, we first use the greedy search algorithm mentioned above to select a subset of input sentences, then use TextRank (Mihalcea and Tarau, 2004) to extract keywords from these sentences. We also filter the keywords that are not in the target summary. The remaining keywords are then fed to our models. For automatic prediction, we use another neural model (BertAbs (Liu and Lapata, 2019) in the experiments) to predict the keywords in the target summary. Relations. Relations are typically represented in the form of relational triples, with each triple containing a subject, a relation, and an object. For example, Barack Obama was born in Hawaii will create a triple (Barack Obama, was born in, Hawaii) . For oracle extraction, we first use Stanford Ope-nIE (Angeli et al., 2015) to extract relational triples from the source document. Similar to how we select highlighted sentences, we then greedily select a set of relations that have the highest ROUGE score with the reference, which are then flattened and treated as guidance. For automatic prediction, we use another neural model (similarly, BertAbs) to predict the relation triples on the target side. Retrieved Summaries. Intuitively, gold summaries of similar documents with the input can provide a reference point to guide the summarization. Therefore, we also try to retrieve relevant summaries from the training data (cid:104)X , Y(cid:105) . For oracle extraction, we directly retrieve five datapoints {(cid:104) x 1 , y 1 (cid:105) , . . . , (cid:104) x 5 , y 5 (cid:105)} from training data whose summaries y i are most similar to the target summary y using Elastic Search. 2 For automatic prediction at test time, we retrieve five datapoints whose source documents x i are most similar to each input source document x instead. We experiment on 6 datasets (statistics in Table 2): Reddit (Kim et al., 2019) is a highly abstractive dataset and we use its TIFU-long version. XSum (Narayan et al., 2018a) is an abstractive dataset that contains one-sentence summaries of online articles from BBC. PubMed (Cohan et al., 2018) is relatively extractive and is collected from scientific papers. CNN/DM (Hermann et al., 2015; Nallapati et al., 2016) is a widely-used summarization dataset consisting of news articles and associated highlights as summaries. We use its non-anonymized version. WikiHow (Koupaee and Wang, 2018) is extracted from an online knowledge base and requires high level of abstraction. New York Times (NYT) (Sandhaus, 2008) is a dataset that consists of news articles and their associated summaries. 3 We follow Kedzie et al. (2018) to preprocess and split the dataset. Our baselines include the following models: BertExt (Liu and Lapata, 2019) is an extractive model whose parameters are initialized with BERT (Devlin et al., 2019). BertAbs (Liu and Lapata, 2019) is an abstractive model with encoder initialized with BERT and trained with a different optimizer than its decoder. MatchSum (Zhong et al., 2020) is an extractive model that reranks the candidate summaries produced by BertExt and achieves state-of-the-art extractive results on various summarization datasets. BART (Lewis et al., 2020) is an state-of-the-art abstractive summarization model pretrained with a denoising autoencoding objective. We build our models based on both BertAbs and BART, and follow their hyperparameter settings to train our summarizers. For our model built on BertAbs, there are 13 encoding layers, with the top layer randomly initialized and separately trained 3 https://catalog.ldc.upenn.edu/ LDC2008T19 Model Guide R-1 R-2 R-L BertExt  (Base) -43.25 20.24 39.63 BertAbs  -41.72 19.39 38.76 BertAbs (Ours) -41.58 18.99 38.56 Ours BertAbs + Sentence Auto. between the two encoders. For our model built on BART, there are 24 encoding layers, with the top layer initialized with pretrained parameters yet separately trained between the two encoders. The first cross-attention block of the decoder is randomly initialized whereas the second cross-attention block is initialized with pretrained parameters. BertAbs is used to predict guidance signals of relations and keywords during test time. Unless otherwise stated, we use oracle extractions at training time. We first compare different kinds of guidance signals on the CNN/DM dataset using BertAbs, then evaluate the best guidance on the other five datasets using both BertAbs and BART. Performance of Different Guidance Signals. As shown in Table 3, if we feed the model with automatically constructed signals, feeding either highlighted sentences or keywords can outperform the abstractive summarization baseline by a large margin. Especially, feeding highlighted sentences can outperform the best baseline by more than 1 Model R-1 R-2 R-L Oracle 55.76 33.22 51.83 Extractive BertExt (Base)  43.25 20.24 39.63 BertExt (Large)  43.85 20.34 39.90 MatchSum  44.41 20.86 40.55 Abstractive BertAbs  41.72 19.39 38.76 BertAbs (Ours) 41.58 18.99 38.56 BertExtAbs  42.13 19.60 39.18 BART  44.16 21.28 40.90 BART (Ours) 44.66 21.53 41.35 Ours BertAbs + BertExt 43.78 20.66 40.66 BART + MatchSum 45.94 22.32 42.48 Table 4: Comparisons with state-of-the-art models on CNN/DM. ROUGE-L point. Using relations or retrieved summaries as guidance will not improve the baseline performance, likely because it is hard to predict these signals during test time. If we use an oracle to select the guidance signals, all varieties of guidance can improve the baseline performance significantly, with the best-performing model achieving a ROUGE-1 score of 55.18. The results indicate that 1) the model performance has the potential to be further improved given a better guidance prediction model; 2) the model does learn to depend on the guidance signals. Comparisons with State of the Art. We then try to build our model on the state-of-the-art model, using highlighted sentences as guidance as it achieves the best performance on CNN/DM. First, we build our model on BART and train it with oracle-extracted highlighted sentences as guidance. Then, we use MatchSum to predict the guidance at test time. From Table 4, we can see that our model can achieve over 1 ROUGE-1/L point improvements compared with the state-of-the-art models, indicating the effectiveness of the proposed methods. Performance on Other Datasets. We report the performance of the highlighted sentence model on all the other five datasets in Table 5. Generally, the model works better when the dataset is more extractive. For abstractive datasets such as Reddit and XSum, our model cannot achieve performance increases when the abstractive summarization base-1-grams 2-grams 3-grams 0 10 20 30 N o v e l n g r a m s % 1-grams 2-grams 3-grams 0 1 2 R ec a ll o f N o v e l n g r a m s % BertAbs Sentence Keyword Relation Retrieve Figure 3: Our model can generate more novel words and achieve higher recall of novel words in the gold reference compared with baseline. line is already rather strong. For extractive datasets such as PubMed and NYT, on the other hand, our model can achieve some improvements over the baselines even though the abstractive baseline outperforms the extractive oracle model in some cases. We perform extensive analyses on CNN/DM to gain insights into our (BERT-based) models. Unless otherwise stated, we use oracle extractions at training time and automatic prediction at test time. Novel n -grams. While we sometimes provide information extracted from the source document as guidance signals, it is unclear whether the model will over-fit to and regurgitate this guidance, or still generate novel expressions. To measure this, we count the number of novel n -grams in the output summaries, namely n -grams that do not appear in the source document. As shown in Figure 3, all of our guided models in fact generate more novel n grams than the baseline, likely because at training time the model is trained to compress and paraphrase the extracted information from the source document into the gold summary. In addition, our models cover more novel n -grams that are in the gold reference than baseline. The results indicate that our guided models can indeed generate novel expressions, and are not referencing the input guidance too strongly. Complementarity of Different Guidance Signals. While some guidance signals achieve worse performance than others, it is still possible to aggregate their outputs and obtain better performance if their outputs are diverse and they complement each-other. To verify this hypothesis, we try to select the best output of the four guidance signals for each test datapoint and investigate if we can aggregate their best outputs and achieve better performance. score of each output of the four guidance signals and pick the best one. As shown in Table 6, despite the fact that the highlighted sentence signal achieves the best overall performance, it still under-performs one of the other three varieties of guidance more than 60% of the time. In addition, by aggregating their best outputs together, we can achieve a ROUGE-1/L point of 48.30/45.15, which significantly outperforms any single guided model. Further, we try to aggregate these guidance signals in a pairwise manner, and Table 7 demonstrates that each guidance signal is complementary to each other to some extent. Thus, we can safely conclude that each type of guidance signal has its own merits and one promising direction is to utilize a system combination method such as Hong et al. (2015) to aggregate the results together. Controllability. It is also of interest what effect this guidance has on the model outputs qualitatively. We sample several generated outputs (Table 8) and find that different provided signals can result in different outputs. Especially, for our sentence-guided model, providing the model with by running tissue paper over his son seth makes him sleep enables the model to generate the exact same sentence, and when the model is fed with one grateful viewer of the video commented... , it will generate one viewer commented... . The examples demonstrate that our model can generate summaries mostly faithful to the guidance signals while also performing abstraction. Faithfulness of Generated Summaries. We also evaluate whether our generated summaries are faithful to the source document. We randomly sample 100 datapoints from the test set and ask 3 people from Amazon Mechanical Turk to evaluate their factual correctness. Each person gives a score between 1 and 3, with 3 being perfectly faithful to the source document. Table 9 shows that our guided model can generate more faithful summaries compared with the baseline. Necessity of Using Oracles During Training. As mentioned previously, we use an oracle to select guidance signals during training. In this part, we investigate if we can provide automatically constructed guidance to the model during training as well. Table 10 shows that this methodology will lead to significantly worse performance. We con-Model Guidance Output Ref. nathan dailo has found a way to get his son to sleep in 42 seconds. jecture that this is because when the relevancy between guidance and reference is weakened, the model will not learn to depend on the guidance signals and thus the model will be reduced to the original abstractive summarization baseline. We propose a general framework for guided neural summarization, using which we investigate four types of guidance signals and achieve state-of-the-art performance on various popular datasets. We demonstrate the complementarity of the four guid-Train Test R-1 R-2 R-L Oracle Auto 43.78 20.66 40.66 Oracle 55.18 32.54 52.06 Auto Auto 41.61 19.04 38.65 Oracle 43.07 20.79 40.13 Table 10: Using automatically constructed guidance during training degrades the performance significantly. ance signals, and find that our models can generate more novel words and more faithful summaries. We also show that we can control the output by providing user-specified guidance signals. Given the generality of our framework, this opens the possibility for several future research directions including 1) developing strategies to ensemble models under different guidance signals; 2) incorporating sophisticated techniques such as copy or coverage over the source document, the guidance signal, or both; and 3) experimenting with other kinds of guidance signals such as salient elementary discourse units. We thank Shruti Rijhwani, Yiran Chen, Jiacheng Xu and anonymous reviewers for valuable feedback and helpful suggestions. This work was supported in part by a grant under the Northrop Grumman SO-TERIA project and the Air Force Research Laboratory under agreement number FA8750-19-2-0200. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Air Force Research Laboratory or the U.S. Government.
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