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7.4 Summarization

Karen Sparck Jones
University of Cambridge, Cambridge, UK

Automatic abstracting was first attempted in the 1950s, in the form of Luhn's auto-extracts, (cf. [Pai90]); but since then there has been little work on, or progress made with, this manifestly very challenging task. However the increasing volume of machine-readable text, and advances in natural language processing, have stimulated a new interest in automatic summarizing reflected in the 1993 Dagstuhl Seminar, Summarizing text for intelligent communication [ENHSJ95]. Summarizing techniques tested so far have been limited either to general, but shallow and weak approaches, or to deep but highly application-specific ones. There is a clear need for more powerful, i.e., general but adaptable, methods. But these must as far as possible be linguistic methods, not requiring extensive world knowledge, and ones able to deal with large-scale text structure as well as individual sentences.

7.4.1 Analytical Framework

Work done hitherto, relevant technologies, and required directions for new research are usefully characterized by reference to an analytical framework covering both factors affecting summarizing and the essential summarizing process. I shall concentrate on text, but the framework applies to discourse in general including dialogue.

A summary text is a derivative of a source text condensed by selection and/or generalization on important content. This is not an operational definition, but it emphasizes the crux of summarizing, reducing whole sources without requiring pre-specification of desired content, and allows content to cover both information and its expression. This broad definition subsumes a very wide range of specific variations. These stem from the context factors characterizing individual summarizing applications. Summarizing is conditioned by input factors categorizing source form and subject; by purpose factors referring to audience and function; and also, subject to input and purpose constraints, by output factors including summary format and style.

The global process model has two major phases: interpretation of the source text involving both local sentence analysis and integration of sentence analyses into an overall source meaning representation ; and generation of the summary by formation of the summary representation using the source one and subsequent synthesis of the summary text. This logical model emphasizes the role of text representations and the central transformation stage. It thus focuses on what source representations should be like for summarizing, and on what condensation on important content requires. Previous approaches to summarizing can be categorized and assessed, and new ones designed, according to (a) the nature of their source representation, including its distance from the source text, its relative emphasis on linguistic, communicative or domain information and therefore the structural model it employs and the way this marks important content; and (b) the nature of its processing steps, including whether all the model stages are present and how independent they are.

7.4.2 Past Work

For instance, reviewing past work (see [Pai90,SJ93]), source text extraction using statistical cues to select key sentences to form summaries is taking both source and summary texts as their own linguistic representations and also essentially conflating the interpretation and generation steps. Approaches using cue words as a base for sentence selection are also directly exploiting only linguistic information for summarizing. When headings or other locational criteria are exploited, this involves a very shallow source text representation depending on primarily linguistic notions of text grammar, though [L93] has a richer grammar for a specific text type.

Approaches using scripts or frames on the other hand [YH85,DeJ79] involve deeper representations and ones of an explicitly domain-oriented kind motivated by properties of the world. DeJong's work illustrates the case where the source representation is deliberately designed for summarizing, so there is little transformation effort in deriving the summary template representation. In the approach of [Rau88], however, the hierarchic domain-based representation allows generalization for summarizing.

There has also been research combining different information types in representation. Thus [Hah90] combines linguistic theme and domain structure in source representations, and seeks salient concepts in these for summaries.

Overall in this work, source reduction is mainly done by selection: this may use general, application-independent criteria, but is more commonly domain-guided as in [MHG84], or relies on prior, inflexible specification of the kind of information sought, as with [DeJ79], which may be as tightly constrained as in MUC. There is no significant condensation of input content taken as a whole: in some cases even little length reduction. There has been no systematic comparative study of different types of source representation for summarizing, or of context factor implications. Work hitherto has been extremely fragmentary and, except where it resembles indexing or is for very specific and restricted kinds of material, has not been very successful. The largest-scale automatic summarizing experiment done so far has been DeJong's, applying script-based techniques to news stories. There do not appear to be any operational summarizing systems.

7.4.3 Relevant Disciplines

The framework suggests there are many possibilities to explore. But given the nature and complexity of summarizing, it is evident that ideas and experience relevant to automatic summarizing must be sought in many areas. These include human summarizing, a trained professional skill that provides an iterative, processual view of summarizing often systematically exploiting surface cues; discourse and text linguistics supplying a range of theories of discourse structure and of text types bearing on summarizing in general, on different treatments suited to different source types, and on the relation between texts, as between source and summary texts; work on discourse comprehension, especially that involving or facilitating summarizing; library and information science studies of user activities exploiting abstracts e.g., to serve different kinds of information need; research on user modeling in text generation, for tailoring summaries; and NLP technology generally in supplying both workhorse sentence processing for interpretation and generation and methods for dealing with local coherence, as well as results from experiments with forms of large-scale text structure, if only for generation so far, not recognition. Some current work drawing on these inputs is reported in [IPM95]; it also illustrates a growing interest in generating summaries from non-text material.

7.4.4 Future Directions

The full text revolution , also affecting indexing, implies a pressing need for automatic summarizing, and current NLP technology provides the basic resource for this. There are thus complementary shorter and longer term lines of work to undertake, aimed at both practical systems and a scientific theory of summarizing, as follows:

  1. Develop shallow-processing techniques that exploit robust parsing, and surface or statistical pointers to key topics and topic connections, for simple indexing-type information extracts and summaries.

  2. Seek generalizations of deep, domain-based approaches using e.g., frames, to reduce tight application constraints and extend system scopes.

  3. Carry out systematic experiments to assess the potentialities of alternative types of source representation both for any summarizing strategy and in relation to different context factor conditions.

  4. Engage in user studies to establish roles and hence requirements for summaries as leading to or providing information, and to determine sound methods of evaluating summaries.

  5. Explore dynamic, context-sensitive summarizing for interactive situations, in response to changing user needs as signaled by feedback and as affected by ad hoc assemblies of material.

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Next: 7.5 Computer Assistance in Text Creation and Editing Up: 7 Document Processing Previous: 7.3 Text Interpretation: Extracting Information