TAC Summarization 2011 Task

A TAC Summarization 2011 Task was a TAC summarization task produced by the TAC Summarization 2011 Track.

• Example(s):
  • TAC 2011 Guided Summarization Task (a Guided Summarization Task).
  • TAC 2011 Automatically Evaluating Summaries Of Peers Task.
  • TAC 2011 Multiling Pilot Task.
  • …
• Counter-Example(s)
  • TAC 2010 Summarization Task.
  • TAC-KBP 2011 Task.
• See: DUC Summarization Task.

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References

2011

• http://www.nist.gov/tac/2011/Summarization/ TAC 2011 Summarization Tasks
  • The goal of the Summarization Track is to foster research on systems that produce short, coherent summaries of text. The 2011 Summarization Track has three tasks:
    • 1. Guided Summarization: The goal of guided summarization is to encourage a deeper linguistic (semantic) analysis of the source documents instead of relying only on document word frequencies to select important concepts. The guided summarization task is to write a 100-word summary of a set of 10 newswire articles for a given topic, where the topic falls into a predefined category. Participants (and human summarizers) are given a list of aspects for each category, and a summary must include all aspects found for its category. Additionally, an "update" component of the guided summarization task is to write a 100-word "update" summary of a subsequent 10 newswire articles for the topic, under the assumption that the user has already read the earlier articles. Summaries will be evaluated for readability, content, and overall responsiveness. (The guided summarization task was run in the TAC 2010 Summarization Track.)
    • 2. Automatically Evaluating Summaries Of Peers (AESOP): The AESOP task is to automatically score a summary for a given metric. AESOP complements the basic summarization task by building a collection of automatic evaluation tools that support development of summarization systems. (AESOP was run in the TAC 2010 Summarization Track.)
    • 3. Multiling Pilot: The aim of the MultiLing pilot is to foster and promote the use of multi-lingual algorithms for summarization. This includes the effort of transforming an algorithm or a set of resources from a mono-lingual to a multi-lingual version.

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