Event-Based Extractive Summarization

1. Event-Based Extractive Summarization E. Filatova and V. Hatzivassiloglou Department of Computer Science Columbia University (ACL 2004)

2. Abstract • Most approaches to extractive summarization define a set of features upon which selection of sentences is based, using algorithms independent of the features • We proposed a new set of features based on low-level atomic events that describe relationships between important actors • Our experimental results indicate that not only the event-based features offer an improvement in summary quality over words as features, but that this effect is more pronounced for more sophisticated summarization methods

3. Introduction Identify what information is important and should be included into the summary Break the input text into textual units (sentences, clauses, etc.) information features textual units Score every textual unit according to what information is covered in it rescore the textual units based on what information is already covered by the summary Choose the textual unit that should be added to the summary repeat until we reach the desired length

4. General Summarization Model Ti: text unit I Ci: concept i * Each concept ci has an associated weight wi Indicating the importance

5. General Summarization Model • Using the above matrix it is possible to formulate the extractive summarization problem as extracting the minimal amount of text units which cover all the concepts that are interesting or important • To account for the cost of long summaries, we can constrain the total length of the summary or balance it against the total weight of covered concepts

6. Associating Concepts with Features • Lexical features: • Words: tf*idfweights show what words are important • Words used in titles and section headings (Luhn’59) • Presence of cue phrases in the textual unit: in conclusion, significant (Kupiec et al’95) • Co-occurrence of some particular terms: lexical chains (Barzilay & Elhadad’97), topic signatures (Lin & Hovy’2000) • Non-lexical features: • Textual unit’s position in the input text:head-line, first sentence in the paragraph(Baxedale’58) • Rhetorical representation of the source text (Marcu’97) • We suggest to use atomic events as features signaling out the important sentences

7. Atomic Events Atomic events = Relation + Connector (potential label for the relation) • Relation is a pair of Named Entities or significant nouns • For the input text, get all possible pairs of named entities within one sentence • For every relation analyze all the verbs and action defining nouns in-between the named entities in the relation, these verbs/nouns can be used as labels for the extracted relations • Some important words are not marked by name entities but are highly likely to be among the most frequently used nouns • Top ten most frequent nouns are added to the relation list

8. Atomic Events • Algorithm for atomic event extraction • Analyze each input sentence one at a time; ignore sentences that do not contain at least two name entities or frequent nouns • Extract all the possible pairs (relations) of named entities / frequent nouns in the sentence and words in-between the name entities (connectors) • For each relation, count how many times this relation is used in the input texts • Keep only connectors that are content verbs or action nouns, according to Word-Net’s noun hierarchy. For each connector calculate how many times it is used for the extracted relations

9. Atomic Events • Calculate normalized frequencies for all relations Normalized frequency of a relation = n/N, where n – frequency of the current relation in a topic N – overall frequency of all relations in a topic • Calculate normalized frequencies for all connectors Normalized frequency of a connector = c/S,where c – frequency of the current connector in a relation S – overall frequency of all connectors for a relation

10. Atomic Events

11. Atomic Events

12. ( ) * ( ) Normalized freq. of the connector Normalized freq. of the relation Atomic Event Score = Atomic Events • Atomic event score: • The score of the atomic event predicts how well the important this atomic event for the collection of texts is

13. Textual Unit Selection • Static Greedy Algorithm • For every textual units, calculate the weight of this text unit as the sum of the weights of all the concepts covered by this textual unit • Choose the text unit with maximum weight and add it to the final output • Continue extracting other textual units in order of total weight till we get the summary of the desired length

14. Textual Unit Selection • Adaptive Greedy Algorithm • For each textual calculate its weight as the sum of weights of all concepts it covers • Choose the textual unit with the maximum weight and add it to the output. Add the concepts covered by this textual unit to the list of concepts covered in the final output. • Recalculate the weights of text units: subtract from each unit’s weight the weight of all concepts in it that are already covered in the output • Continue extracting text units in order of their total weight (back to step 2) until the summary is of the desired length

15. Textual Unit Selection • Modified Adaptive Greedy Algorithm • For every textual unit calculate its weight as the sum of weights of all concepts it covers • Consider only those textual units that contain the concept with the highest weight that has not yet been covered. Out of these, choose the one with highest total weight and add it to the final output. Add the concepts which are covered by this textual unit to the list of concepts covered in the final output. • Recalculate the weights of text units: subtract from each unit’s weight the weight of all concepts in it that are already covered in the output • Continue extracting text units in order of their total weight (back to step 2) until the summary is of the desired length

16. Experiment • Input Data • The document sets used in the evaluation of multi-document summarization during the first Document Understanding Conference (DUC2001) • 30 test document sets, each with approximately 10 news stories on different events • For each document set three human-constructed summaries are provided for each of the target lengths 50, 100, 200 and 400 words

17. Experiment • Evaluation Metric • ROUGE (Lin and Hovy, 2003) • Recall-based measure • Summary Length • For each document set, four summaries of length 50, 100, 200 and 400 are created • Rouge evaluation has not yet been tested extensively , and ROUGE scores are difficult to interpret as they are not absolute and not comparable across source document sets

18. Experiment • Results: Static Greedy Algorithm

19. Experiment • Results: Adaptive Greedy Algorithm

20. Experiment

21. Experiment • Results: Modified Greedy Algorithm

22. Experiment • Results: Comparison with DUC systems • In DUC2003 the task was to create summaries only of length 100 • Events as features and adaptive greedy algorithm • In 14 out of 30 cases our system outperforms the median of the ROUGE scores of all the 15 participating systems over that specific document set • The suitability of the event-based summarizer varies according to the type of documents being summarized

23. Conclusion • Our experimental results indicate that events are indeed an effective features, at least in comparison with words • With all three of our summarization algorithms, we achieved a gain in performance when using events • Our approach to defining and extracting events can be improved in many ways • Matching connectors that are similar in meaning • Representingparaphrasesof the same event • Methods for detecting and prioritizing special event components such as time and location phrases • Merging information across related atomic events • Partial matches between atomic events and input sentences