Call for papers

Automatic text summarization (TS) has been a matter of active research for over a decade now. Doing TS really well would require insights from statistics, machine learning, linguistics and cognitive science, to name a few. Despite a great deal of research effort, state-of-the-art TS systems achieve summary quality much lower than even untrained human summarizers. There is room for improvement and much interesting work to do.

Summarization is the theme of Text Analysis Conferences (TAC), an influential annual shared evaluation exercise. It is not uncommon to plan TS work around those annual events, regardless of their somewhat narrow range: they focus on summarizing news. While this workshop is open to relevant work already presented at TAC, it is designed as a venue for research on TS which does not necessarily fit the TAC format. We will welcome articles which discuss summarization of other genres (such as blogs, email messages, books, captions or subtitles), investigation of human recall and summarization of data, and the role of language generation in TS, among others. We will also gladly consider position papers on more fundamental long-term challenges in TS: how to move past heavy reliance on shallow lexical information, how to create summaries of high linguistic quality, and so on.

We invite original unpublished contributions on all aspects of TS, including:

• the role of linguistic information and semantic processing in TS;

• discovery of salient information in texts;

• discourse structure for TS;

• TS and models of human summarization and discourse processing;

• summarization of long narratives;

• beyond genre differences: event-based TS, abstractive TS, contrastive TS, opinion summarization;

• summary evaluation models, user involvement in evaluation;

• automatic domain modeling for summarization and abstracting;

• user-tailored summaries;

• integration of summarization with end-user tools.