Controllable Text Summarization: Unraveling Challenges, Approaches, and Prospects - A Survey (2024)

Abstract: Generic text summarization approaches often fail to address the specific intent and needs of individual users. Recently, scholarly attention has turned to the development of summarization methods that are more closely tailored and controlled to align with specific objectives and user needs. Despite a growing corpus of controllable summarization research, there is no comprehensive survey available that thoroughly explores the diverse controllable attributes employed in this context, delves into the associated challenges, and investigates the existing solutions. In this survey, we formalize the Controllable Text Summarization (CTS) task, categorize controllable attributes according to their shared characteristics and objectives, and present a thorough examination of existing datasets and methods within each category. Moreover, based on our findings, we uncover limitations and research gaps, while also exploring potential solutions and future directions for CTS. We release our detailed analysis of CTS papers at https://github.com/ashokurlana/controllable_text_summarization_survey.

## introduction despite the significant advancements in automatic text summarization, its one-size-fits-all approach falls short in meeting the varied needs of different segments of users and application scenarios. for example, generic automatic summarization may struggle to produce easily understandable summaries of scientific documents for non-expert users or create extremely brief summaries of news stories for online feeds. lately, a myriad of works have emerged aimed at generating more controlled @xcite @xcite @xcite and tailored text summaries that meet a wide range of user needs. cts task is centered around creating summaries of source documents that adhere to specific criteria. these criteria are managed through various controllable attributes (ca) or aspects like summary length @xcite , writing style @xcite , coverage of key information @xcite , content diversity @xcite , and more. these criteria vary depending on the task, user needs, and specific application context. for example, length-controlled summaries @xcite are particularly useful in situations where brevity is crucial, like in social media posts, headlines, and abstracts. in areas such as marketing, academic writing, or professional communication, a style-controlled summary @xcite is essential to ensure that the information aligns with the intended tone and messaging strategy. similarly, topic-controlled summaries @xcite are commonly used in research papers, reports, and content curation, providing an emphasis on a specific topic to enhance clarity and coherence in the presented information. there is an uneven distribution of attention within the research community towards various cas as depicted in fig 1 . the majority of cts works concentrate on managing length, topic, and style. this could be attributed to two main factors. first, it is comparatively simpler to develop datasets for evaluating length, topic, and style compared to aspects like structure and diversity. second, there is a plethora of application scenarios for source: (cnn)novak djokovic extended his current winning streak to 17 matches after beating thomas berdych 7-5, 4-6, 6-3 in the rain-interrupted final of the monte carlo masters....after winning the australian open back in january, djokovic has followed up with masters' victories at indian wells and miami. he then beat rafa nadal, arguably one of the greatest players on clay of all time... length:long, coverage:high, topic:djockovic,final summary: djokovic wins 7-5, 4-6, 6-3 after a tight match with berdych in the monte carlo masters final. djokovic also followed up with masters' victories at indian wells and miami. length:normal, topic:djockovic, coverage:normal, summary: it's been a sensational year for djokovic after beating berdych in the finals and also winning against clay expert nadal. length:short, coverage:normal, topic: no control summary: djokovic wins monte carlo masters after beating berdych 7-5, 4-6, 6-3 in the finals. in this survey, we collect and analyze 61 research papers pertaining to various possible cas. the filtration criteria for the selection of papers are described in appenedix a. subsequently, we classify these cas into 10 categories, grouping similar ones based on shared characteristics and objectives. moreover, we delve into the existing datasets, evaluating their creation methods and appropriateness for the respective task in each ca category. furthermore, we scrutinize the current cts methodologies for each ca category, drawing comparisons between their overarching frameworks and discussing relevant limitations. subsequently, we discuss in detail the generic and specific evaluation strategies for cas utilized by various works. finally, we attempt to critique the current approaches and unravel potential future research trajectories. to the best of our knowledge, it is the first comprehensive survey on cts. ## task formulation this section introduces the controllable text summarization (cts) task by outlining its definition and offering a categorized breakdown of different controllable attributes along with concise descriptions for each. given a set of source documents @xmath0, d 2 , . . . , d k }. each document, d i , consists of a sequence of n tokens: {x i,1 , x i,2 , . . . , x i,n }. s i is the target summary of document d i , which comprises of a sequence of m attribute definition length controlling the length of the summary style controlling the readability levels, politeness, humor, and emotion coverage controlling the salient information in summary entity summary specific to pre-defined entities structure create summaries with predefined structure or order abstractivity controlling the novelty in sentence formation salience adjusting the presence of prominent information role providing role-specific summaries diversity generating semantically diverse summaries topic controlling topic-focused summary generation ## datasets this section provides a broad overview of the cts datasets and corresponding creation/acquisition strategies. the cts methods are evaluated in several ways: 1) by utilizing publicly available summarization datasets, 2) by datasets derived from generic datasets, and 3) by creating humanannotated datasets. ## approaches to controlled summarization various cas have been investigated in controllable summary generation tasks, including style (politeness, humor, formality), content (length, entities, keywords), and structure. in this section, we describe various approaches to achieve cts for the attributes mentioned in abstractivity. it measures the degree of textual novelty between the source text and summary. see et al. (2017) introduce a pointer-generator network to control the source copying via pointing and generate novel sentence formations by using generator mechanism. however, this scheme fails to generate higher abstraction levels. kryściński et al. (2018) tackle this problem in two ways: 1) decompose the decoder into a contextual network to retrieve the relevant parts of the text and generate the summary by utilizing a pretrained model, 2) a mixed rl-based objective jointly optimizes the n-gram overlap with the ground truth summary. similarly, song et al. (2020) control the copying behavior by using a mix-and-match strategy to generate summaries with varying n-gram copy rates. based on the seen, unseen words from the source text, the system controls the copying percentage by acting as a language modeling task. moreover, methods such as controlsum (fan et al., 2018a) allow the users to explicitly specify the control attribute to facilitate better control. however, it does not provide any supervision on violating the controllability. to alleviate this issue, chan et al. (2021) propose an rl-based framework on the constrained markov decision process and introduced a reward to penalize the violation of attribute requirement. salience. this attribute captures the most important information in a document. in @xcite , salience is modeled as a feature in a classification objective. it uses gru-based encoders and decoders to frame summarization as a text-to-binary sequence learning task at the sentence level @xcite . a binary score is assigned to each sentence, indicating its membership in the summary. the system performs poorly on out-ofdomain datasets. to retain key content from the source, li et al. (2018) introduce a key information guide network, where keywords are identified by the textrank algorithm with a modified attention mechanism that accommodates this key information as an additional input. however, it focuses mostly on informativeness ignoring coherence and readability features. deutsch and roth (2023) model salience in terms of noun phrases using qa signals where the generation of the summary is conditioned on these identified phrases. this approach is not applicable to languages for which question generation and question answering models are not available. in long document cls tasks, summarization systems often fail to respond to user queries. to resolve this issue, pagnoni et al. (2023) propose a pre-training approach that involves two tasks of salient information identification from sentences having the highest self rouge score and a question generation system to generate questions whose answers are the salient sentences. role. role-oriented dialogue summarization generates summaries for different roles/agents present in a dialogue (e.g. doctor and patient) (liang et al., 2022). lin et al. (2021) propose the csds dataset (see section 4.3) and benchmark a variety of existing state-of-the-art summarization models for the task of generating agent and user surveys. they find that agent summaries generated by the existing methods lack key information, that needs to be extracted from dialogues of the other role. to bridge this gap, lin et al. (2022) build a role-aware summarization model for two users (agent and user) present in the dataset. they use two separate decoders for generating the user and agent summaries by utilizing user and agent masks. a role attention mechanism is introduced to each decoder so that it can leverage the overall context by attending to the hidden states of the other role. liang et al. (2022) use a role-aware centrality scoring model that computes role-aware centrality scores for each utterance, which measures the relevance between the utterance and the role prompts (signaling whether the summary is for the user or agent). this is then used to reweight the attention scores for each utterance, which is subsequently used by the decoder to generate the summary. diversity. traditional decoding strategies, like beam search, excel at generating single summaries but often struggle to produce diverse ones. techniques such as top-k and nucleus sampling are effective in generating diverse outputs but may sacrifice faithfulness. in response to these challenges, narayan et al. (2022) introduce compositional sampling, a decoding method to obtain diverse summaries. this method initiates by planning a semantic composition (narayan et al., 2021) of the target in the form of entity chains, and then leverages beam search to generate diverse summaries. topic. long documents often cover multiple topics, and a generic summary might not fully encompass the diverse scope. krishna and srinivasan (2018) train a topic-conditioned pointer-generator network (see et al., 2017) by concatenating one hot encoding representation of the topic with the embedding of each token in the input document. however, news categories are used as the predefined topics, that limits the generalization to other tasks. to handle diverse topics, tan et al. (2020) utilize external knowledge sources like wikipedia and concept-net to create a weakly supervised summarization framework compatible with any encoder-decoder architecture. suhara et al. (2020) propose an unsupervised method, where aspect-specific opinions are extracted from a set of reviews by a pre-trained opinion extractor, and the summary of the opinion is generated by a generator model trained to reconstruct the reviews from the opinions. similarly, given a set of reviews for a product (e.g. hotels), amplayo et al. (2021) train a multiple instance learning (mil) model, to extract the predictions for aspect (like cleanliness) codes at the document, sentence, token level (mukherjee et al., 2020). these predicted aspects transform the input such that relevant sentences and keywords along with aspect tokens are fed into the pre-trained t5 (raffel et al., 2020) model. hsu and tan (2021) introduce the task of generating decision-supportive summaries. the focus is on predicting future yelp ratings from the set of reviews using a longformer-based (beltagy et al., 2020) regression model. they propose an iterative algorithm that selects the sentences of the summary from a set of representative sentences. mukherjee et al. (2022) extend topic-focused summarization for multimodal documents by creating a joint image-text context vector. ## evaluation strategies this section catalogs and briefly describes the variety of automatic and human evaluation metrics that are being used to evaluate the summaries generated by the different methods studied in this paper. ## conclusions we present a comprehensive survey on controllable text summarization (cts) by offering a detailed analysis, from formalizing various controllable attributes, classifying them based on shared characteristics, and delving into existing datasets, proposed models, associated limitations, and evaluation strategies. moreover, we discuss the challenges and prospects, making it a helpful guide for researchers interested in cts. we plan to keep the github repository regularly updated with the latest cts works.