CTRLSUM: TOWARDS GENERIC CONTROLLABLE TEXT SUMMARIZATION

Abstract

Current summarization systems yield generic summaries that are disconnected from users' preferences and expectations. To address this limitation, we present CTRLsum, a novel framework for controllable summarization. Our approach enables users to control multiple aspects of generated summaries by interacting with the summarization system through textual input in the form of a set of keywords or descriptive prompts. Using a single unified model, CTRLsum is able to achieve a broad scope of summary manipulation at inference time without requiring additional human annotations or pre-defining a set of control aspects during training. We quantitatively demonstrate the effectiveness of our approach on three domains of summarization datasets and five control aspects: 1) entity-centric and 2) length-controllable summarization, 3) contribution summarization on scientific papers, 4) invention purpose summarization on patent filings, and 5) question-guided summarization on news articles in a reading comprehension setting. Moreover, when used in a standard, uncontrolled summarization setting, CTRLsum achieves state-of-the-art results on the CNN/DailyMail dataset. 1

1. INTRODUCTION

Neural summarization systems aim to compress a document into a short paragraph or sentence while preserving key information. There are largely two categories of summarization systems: extractive summarization that extracts important portions of a document (Cheng & Lapata, 2016; Nallapati et al., 2017; Narayan et al., 2018) , and abstractive summarization that freely generates novel sentences (Rush et al., 2015; See et al., 2017; Paulus et al., 2018) which can produce coherent and fluent summaries more flexibly. In this paper we focus on abstractive summarization. Typically abstractive summarization methods take a document as input and yield a generic summary to cover certain information identified by the model. However, content of interest is user-dependent. Summaries should select information with respect to preferences of a user. For example, Figure 1 shows an NBA basketball news article, and the reference summary describes several match results. However, fans of certain basketball stars in these teams such as Lebron James or Stephen Curry might only be interested in the matches they played and would like to know the player's scores as well. Motivated by this, we focus on controllable summarization which allows the users to manipulate the summaries from the model. We propose CTRLsum, a framework to control summaries through control tokens in the form of a set of keywords or descriptive prompts. At training time, the model learns to predict summaries conditioned on both the source document and keywords that serve as external guidance. During inference, keywords and optional prompts, which are the target prefix to constrain decoding, are combined as control tokens to convey user preferences as shown in Figure 1 . Keywords and prompts are complementary. Prompts do not perform well in many cases such as entity or length controlled summarization as our preliminary experiments imply, but keywords can achieve those goals in a flexible way, for example, by using entity as keywords or varying the number of keywords to control entities and length respectively. However, keywords struggle in more open-ended scenarios like summarizing a list of contributions of scientific papers, while constraining the decoding with prompt "the main contributions of this paper are:(1)" is possibly sufficient to achieve the goal.



Code and model checkpoints will be public after the review period.1

