1. Automatic Timeline Generation Jessica Jenkins Josh Taylor CS 276b

2. Corpus Subset of TDT-3 text news articles 875 articles 49 topics (collections of related articles) 6-30 articles per topic Sources: ABC, APW, CNN, NBC, NYT, VOA Focus on sentence-level event detection within a topic

3. Labeling / Data Processing Manual annotation for evaluation 10 topics, 120 articles Decide on a set of events for each topic Annotate each sentence in each article with a set of relevant topic events Sentence boundary detection MXTerminator: maximum entropy classifier (Reynar and Ratnaparkhi)

4. Data Processing Sentences tokenized with case-folding, punctuation stripping, Porter stemming, and English stop-word removal Part of Speech tagging Stanford Log-linear PoS Tagger (Toutanova and Manning) Noun Phrase Chunking BaseNP Chunking (Ramshaw and Marcus) Only retained verbs for non-NP terms

5. Evaluation Precision and recall analogs Defined relative to set of annotated events NU-precision: count of sentences labeled with novel events (relative to higher- ranked sentences) over number of selected sentences NU-recall: number of events in selected sentences over number of events in topic

6. Event Selection Methods Language model-based sentence scoring (Allan, Gupta, Khandelwal) “ Useful ” score: measure of how likely a sentence is to be on-topic. “ Novel ” score: indication that sentence content is unlike previous sentences Sentences ranked using a weighted blend of their Useful and Novel score

7. Sample Timeline Top 5 sentences by score, ordered chronologically: As Mr. Sharif left the White House, he said the talks focused on the India-Pakistan dispute over the Kashmir region and U.S. concerns about nuclear arms tests conducted by India and Pakistan. In 1989, Pakistan paid the U.S. close to $700 million for 28 F-16 fighter planes. But Pakistan was never refunded for the F-16s. In return, Pakistan will withdraw its claim to the F-16 aircraft. Delivery of the jets was stopped in 1990 because of the U.S. arms embargo against Pakistan because of its nuclear program.

9. Event Selection Methods Event discovery through clustering Similar to text summarization Each cluster represents an event Hierarchical agglomerative clustering with sparse vectors K-means (k=5,10,20) with and without dimensionality reduction

10. Clustering Results Tried using group average HAC without dimensionality reduction Representative sentence chosen for maximal cosine similarity with cluster centroid Underwhelming results with respect to evaluation metrics

11. Sample Timeline 2 Top 5 “ useful ” cluster representatives, taken from 15 clusters, ordered chronologically: Mr. Clinton also said he and Mr. Sharif would try to resolve a dispute over a canceled sale of U.S. fighter aircraft. Mr. Clinton wants India and Pakistan, which each conducted nuclear tests last May, to sign the Comprehensive Test Ban Treaty as they have indicated they would do. All of you know my concern to do everything we can to end the nuclear competition in South Asia which I believe is a threat to Pakistan and India and to the stability of the world. The delivery was blocked under a 1990 law barring direct U.S. military sales to Pakistan because of its development of nuclear weapons. He met with Pakistan's Prime Minister Omar Sharif.

12. Clustering in Progress Using SVD dimensionality reduction Noun phrase features with part-of- speech filtering Representative sentences chosen from each cluster based on Useful score

13. Problem Areas Small annotated corpus Few annotated events per topic give a coarse evaluation Weak correspondence between clusters and labeled events

14. Unexplored Possibilities Detection and exploitation of temporal features in sentences Clustering with different sentence similarity measures WordNet-derived semantic distance