Automatic summarisation: 25 years On

Automatic text summarisation is a topic that has been receiving attention from the research community from the early days of computational linguistics, but it really took off around 25 years ago. This article presents the main developments from the last 25 years. It starts by defining what a summary is and how its definition changed over time as a result of the interest in processing new types of documents. The article continues with a brief history of the field and highlights the main challenges posed by the evaluation of summaries. The article finishes with some thoughts about the future of the field.

Subtopic annotation and automatic segmentation for news texts in Brazilian Portuguese

Subtopic segmentation aims to break documents into subtopical text passages, which develop a main topic in a text. Being capable of automatically detecting subtopics is very useful for several Natural Language Processing applications. For instance, in automatic summarisation, having the subtopics at hand enables the production of summaries with good subtopic coverage. Given the usefulness of subtopic segmentation, it is common to assemble a reference-annotated corpus that supports the study of the envisioned phenomena and the development and evaluation of systems. In this paper, we describe the subtopic annotation process in a corpus of news texts written in Brazilian Portuguese, following a systematic annotation process and answering the main research questions when performing corpus annotation. Based on this corpus, we propose novel methods for subtopic segmentation following patterns of discourse organisation, specifically using Rhetorical Structure Theory. We show that discourse structures mirror the subtopic changes in news texts. An important outcome of this work is the freely available annotated corpus, which, to the best of our knowledge, is the only one for Portuguese. We demonstrate that some discourse knowledge may significantly help to find boundaries automatically in a text. In particular, the relation type and the level of the tree structure are important features.

Automatic summarisation of discussion fora

Web-based discussion fora proliferate on the Internet. These fora consist of threads about specific matters. Existing forum search facilities provide an easy way for finding threads of interest. However, understanding the content of threads is not always trivial. This problem becomes more pressing as threads become longer. It frustrates users that are looking for specific information and also makes it more difficult to make valuable contributions to a discussion. We postulate that having a concise summary of a thread would greatly help forum users. But, how would we best create such summaries? In this paper, we present an automated method of summarising threads in discussion fora. Compared with summarisation of unstructured texts and spoken dialogues, the structural characteristics of threads give important advantages. We studied how to best exploit these characteristics. Messages in threads contain both explicit and implicit references to each other and are structured. Therefore, we term the threads hierarchical dialogues. Our proposed summarisation algorithm produces one summary of an hierarchical dialogue by ‘cherry-picking’ sentences out of the original messages that make up a thread. We try to select sentences usable for obtaining an overview of the discussion. Our method is built around a set of heuristics based on observations of real fora discussions. The data used for this research was in Dutch, but the developed method equally applies to other languages. We evaluated our approach using a prototype. Users judged our summariser as very useful, half of them indicating they would use it regularly or always when visiting fora.