Method and System for Retrieving, Selecting, and Presenting Compelling Stories form Online Sources

• US 20080313130A1
• Filed: 06/14/2007
• Published: 12/18/2008
• Est. Priority Date: 06/14/2007
• Status: Abandoned Application

• Current Assignee: Northwestern University
• Original Assignee: Northwestern University
• Inventors: Hammond, Kristian J., Sood, Sanjay C., Owsley, Sara H.
• Application Number:
  US11/763,324
• Publication Number:
  US 20080313130A1
• Time in Patent Office: Days
• US Class Current: 707/2
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06Q 90/00 Systems or methods specially adapted for administrative, commercial, financial, managerial or supervisory purposes, not involving significant data processing

Abstract

The invention provides a method and system for automatically retrieving, selecting, and presenting compelling stories from online sources. The system mines the online sources and collects texts that are likely to contain compelling stories. The system then extracts candidate stories from them and transforms these candidate stories to make them appropriate for presentation. The candidate stories are then passed through a set of filters to focus the system on stories with a heightened emotional state. Techniques are used to ensure retrieval of appropriate and meaningful content for the performance of the stories. The modified and filtered stories are then prepared for presentation, including marked up with speech and animation cues, gender classification, and dramatic Adaptive Retrieval Charts (or ARCs). These ARCs allow for various performance types from an ongoing performance of multiple actors in a physical installation to single actor performance of a single story for an online system.

Claims

1. A method for providing compelling stories from online sources, comprising:

(a) retrieving documents likely to contain stories from the online sources;

(b) extracting candidate stories from the documents; and

(c) filtering the candidate stories to identify stories with predefined levels of sentiment;

(d) preparing the filtered stories for spoken presentation by animated characters; and

(e) presenting the prepared stories using computer generated speech by the animated characters.

2. The method of claim 1, wherein the retrieving (a) comprises:

(a1) forming queries to retrieve the documents containing structural cues indicative of a type of story; and

(a2) running the queries using search engines.

3. The method of claim 2, wherein the structural cues comprise text or phrases indicating a writer is starting to tell a story.

4. The method of claim 2, wherein the structural cues comprise text or phrases indicating a situational category for the type of story.

5. The method of claim 2, wherein the queries further retrieve the documents matching predefined topics of interest.

6. The method of claim 1, wherein the extracting (b) comprises:

(b1) finding occurrences of query terms and structural cues in the documents; and

(b2) for each occurrence, searching for a first natural breaking point and a second natural breaking point following the first natural breaking point, wherein a section of text between the first and second natural breaking points comprise the candidate story.

7. The method of claim 6, wherein the section of text comprises a complete paragraph.

8. The method of claim 1, wherein the filtering (c) comprises:

(c1) evaluating relevance of the candidate stories to structural cues used in the retrieval of the documents.

9. The method of claim 8, wherein for each candidate story, the evaluating (c1) comprises:

(c1i) determining if the structural cues are present in the candidate story;

(c1ii) determining if the structural cues appear in a first sentence of the candidate story; and

(c1iii) eliminating the candidate story if the structural cues are not present in the candidate story or if the structural cues do not appear in the first sentence.

10. The method of claim 9, wherein for each candidate story, the evaluating (c1) further comprises:

(c1iv) phrasally analyzing the candidate story according to a topic of interest used in the retrieval of the documents; and

(c1v) eliminating the candidate story if the candidate story is not sufficiently on point with the topic of interest.

11. The method of claim 1, wherein the filtering (c) comprises:

(c1) filtering the candidate stories by syntax to eliminate candidate stories comprising syntactical indicators that the candidate story is not a narrative.

12. The method of claim 1, wherein the filtering (c) comprises:

(c1) performing sentiment analysis on the candidate stories to classify the candidate stories based on affective valence; and

(c2) eliminating the candidate stories that are not within a predetermined range of affective valence.

13. The method of claim 12, wherein the performing (c1) comprises:

(c1i) labeling documents within a corpus with a sentiment rating;

(c1ii) removing the documents within the corpus labeled with a neutral sentiment rating;

(c1iii) building a statistical representation of the remaining documents in the corpus, wherein the remaining documents in the corpus are separated into a positive group and a negative group;

(c1iv) creating an affect query as a representation of a target candidate story, wherein the affect query is created by selecting words in the target candidate story that exhibit the greatest statistical variance between the positive and the negative documents in the statistical representation;

(c1v) using the affect query to retrieve affectively similar documents from the corpus; and

(c1vi) combining the labels from the retrieved documents to derive an affect score for the target document.

14. The method of claim 13, wherein the eliminating (c2) comprises:

(c2i) if the affect score is not within a predetermined range of values, then eliminating the target candidate story.

15. The method of claim 1, wherein the filtering (c) comprises:

(c1) determining a number of web pages on which each word in the candidate stories appears;

(c2) determining a score for how familiar each word is based on the number;

(c3) determining colloquial thresholds based on a distribution of the scores for the words in the candidate stories;

(c4) for each candidate story, determining if the candidate story meets the colloquial thresholds; and

(c5) eliminating the candidate story, if the candidate story does not meet the colloquial thresholds.

16. The method of claim 1, wherein the filtering (c) comprises:

(c1) for each candidate story, determining if the candidate story comprises undesirable language; and

(c2) eliminating the candidate story, if the candidate story comprises undesirable language.

17. The method of claim 1, wherein the filtering (c) comprises:

(c1) eliminating candidate stories that comprise problematic syntax for text-to-speech engines.

18. The method of claim 17, wherein the problematic syntax comprises poor punctuation, too many numbers, numbers with many digits, URLs, links, email addresses, or direct quotes.

19. The method of claim 1, wherein for each candidate story, the filtering (c) comprises:

(c1) identifying indicators of a gender of an author of the candidate story, wherein the indicators comprise self-referential roles, physical states, and relationships;

(c2) determining if the indicators agree on the gender of the author; and

(c3) if the indicators agree on the gender of the author, then classifying the candidate story with the gender.

20. The method of claim 1, wherein the filtering (c) comprises:

(c1) modifying the candidate stories to improve readability by a text-to-speech engine.

21. The method of claim 20, wherein the modifications can comprise:

removal of any parenthetical, bracketed or braced content,

condensation of adjacent punctuation,

alteration any numbers, dates, or monetary amounts to be readable by the text-to-speech engine, and

expansion of acronyms or abbreviations.

22. The method of claim 1, wherein the preparing (d) comprises:

(d1) structuring the presentation using dramatic Adaptive Retrieval Charts (ARCs), wherein the ARCs comprise instructions for the retrieving (a), extracting (b), and filtering (c) based on a goal set.

23. The method of claim 1, wherein for each filtered candidate story, the preparing (d) comprises:

(d1) determining which sentences of the filtered candidate story are highly affective and which emotion the sentences are characterized by; and

(d2) marking up the highly affective sentences, such that the marked up sentences have more emphasis in a presentation of the computer generated speech and the animated characters.

24. The method of claim 23, wherein the marking up comprises marking up of a volume, rate, or pitch, or inserting pauses.

25. A method for providing compelling stores from online sources, comprising:

(a) forming queries to retrieve documents from the online sources containing query terms and structural cues indicative of a type of story;

(b) running the queries using search engines;

(c) finding occurrences of the query terms and structural cues in the retrieved documents; and

(d) for each occurrence, searching for a first natural breaking point and a second natural breaking point following the first natural breaking point, wherein a section of text between the first and second natural breaking points comprise a candidate story.

26. The method of claim 25, wherein the structural cues comprise text or phrases indicating a writer is starting to tell a story.

27. The method of claim 25, wherein the structural cues comprise text or phrases indicating a situational category for the type of story.

28. The method of claim 25, wherein the queries further retrieve the documents matching predefined topics of interest.

29. The method of claim 25, wherein the section of text comprises a complete paragraph.

30. A method for providing compelling stories from online sources:

(a) obtaining candidate stories extracted from documents retrieved from the online sources, wherein the documents are retrieved using a query comprising query terms and structural cues indicative of a type of story;

(b) for each candidate story, determining if the structural cues are present;

(c) for each candidate story, determining if the structural cues appear in a first sentence; and

(d) eliminating the candidate stories in which the structural cues are not present or where the structural cues do not appear in the first sentence.

31. The method of claim 30, wherein the queries further retrieve the documents matching predefined topics of interest, wherein the method further comprises:

(e) for each candidate story, phrasally analyzing the candidate story according to the topics of interest; and

(f) eliminating the candidate stories that are not sufficiently on point with the topics of interest.

32. A method for providing compelling stories from online sources, comprising:

(a) obtaining candidate stories extracted from the online sources;

(b) labeling documents within a corpus with sentiment ratings;

(c) removing the documents within the corpus labeled with a neutral sentiment rating;

(d) building a statistical representation of the remaining documents in the corpus, wherein the remaining documents in the corpus are separated into a positive group and a negative group;

(e) creating an affect query as a representation of a target candidate story, wherein the affect query is created by selecting words in the target candidate story that exhibit the greatest statistical variance between the positive and the negative documents in the statistical representation;

(f) using the affect query to retrieve affectively similar documents from the corpus;

(g) combining the labels from the retrieved documents to derive an affect score for the target candidate story; and

(h) if the affect score is not within a predetermined range of values, then eliminating the target candidate story from the candidate stories.

33. A method for providing compelling stories from online sources, comprising:

(a) obtaining a candidate story extracted from the online sources;

(b) identifying indicators of a gender of an author of the candidate story, wherein the indicators comprise self-referential roles, physical states, and relationships;

(c) determining if the indicators agree on the gender of the author;

(d) if the indicators agree on the gender of the author, then classifying the candidate story with the gender;

(e) presenting the candidate story using computer generated speech by an animated character with the gender.

34. A method for providing compelling stories from online sources, comprising:

(a) obtaining candidates stories extracted from the online sources;

(b) modifying the candidate stories to improve readability by a text-to-speech engine, wherein the modifications comprise;

removal of any parenthetical, bracketed or braced content,

condensation of adjacent punctuation,

alternation of any numbers, date, or monetary amounts to be readable by the text-to-speech engine, and

expansion of acronyms or abbreviations; and

(c) presenting the modified candidate stories using computer generated speech by animated characters.

35. A method for providing compelling stories from online sources, comprising:

(a) obtaining candidate stories extracted from the online sources;

(b) determining which sentences of the candidate stories are highly affective and which emotion the sentences are characterized by;

(c) marking up the highly affective sentences, such that the marked sentences have more emphasis in a presentation of computer generated speech by animated characters; and

(d) presenting the marked up stories using the computer generated speech by the animated characters.

36. The method of claim 35, wherein the marking up comprises marking up of a volume, rate, or pitch, or inserting pauses.

37. A system for providing compelling stories from online sources, comprising:

a retrieval engine for retrieving documents likely to contain stories from the online sources and for extracting candidate stories from the documents;

a filtering and modification engine for filtering the candidate stories to identify stories with predefined levels of sentiment and for preparing the filtered stories for spoken presentation by animated characters; and

a presentation engine for presenting the prepared stories using computer generated speech by animated characters.

38. The system of claim 37, wherein the retrieval engine forms queries to retrieve the documents containing structural cues indicative of a type of story and runs the queries using search engines.

39. The system of claim 37, wherein the retrieval engine finds occurrences of query terms and structural cues in the documents, and for each occurrence, searches for a first natural breaking point and a second natural break point following the first natural breaking point, wherein a section of text between the first and second natural breaking points comprise the candidate story.

40. The system of claim 37, wherein the filtering and modification engine comprises story filters for evaluating relevance of the candidate stories to structural cues used in the retrieval of the documents.

41. The system of claim 37, wherein the filtering and modification engine comprises story filters for filtering the candidate stories by syntax to eliminate candidate stories comprising syntactical indicators that the candidate story is not a narrative.

42. The system of claim 37, wherein the filtering and modification engine comprises content or impact filters for performs sentiment analysis on the candidate stories to classify the candidate stories based on affective valence, and eliminating the candidate stories that are not within a predetermined range of affective valence.

43. The system of claim 37, wherein the filtering and modification engine comprises colloquial filtering for determining a number of web pages on which each word in the candidate stories appears, determining a score for how familiar each word is based on the number, determining colloquial thresholds based on a distribution of the scores for the words in the candidate stories, for each candidate story determining if the candidate story meets the colloquial thresholds, and eliminating the candidate story if the candidate story does not meet the colloquial thresholds.

44. The system of claim 37, wherein the filtering and modification engine comprises a language filter for determining if the candidate story comprise undesirable language, and eliminating the candidate story if the candidate story comprises undesirable language.

45. The system of claim 37, wherein the filtering and modification engine comprises presentation filters for eliminating candidate stories that comprise problematic syntax for text-to-speech engines.

46. The system of claim 37, wherein for each candidate story, the filtering and modification engine identifies indicators of a gender of an author of the candidate story, wherein the indicators comprise self-referential roles, physical states, and relationships, determines if the indicators agree on the gender of the author, and if the indicators agree on the gender of the author, then classifying the candidate story with the gender.

47. The system of claim 37, wherein the filtering and modification engine comprises presentation modifiers for modifying the candidate stories to improve readability by a text-to-speech engine.

48. The system of claim 37, wherein the presentation engine structures the presentation using dramatic Adaptive Retrieval Charts (ARCs), wherein the ARCs comprise instructions for retrieving, extracting, and filtering based on a goal set.

49. The system of claim 37, wherein for each filtered candidate story, the presentation engine determines which sentences of the filtered candidate stories are highly affective and which emotion the sentences are characterized by, and marking up the highly affective sentences such that the marked up sentences have more emphasis in a presentation of the computer generated speech and the animated characters.

50. A computer readable medium with program instructions for providing compelling stories from online sources, comprising instructions for:

(a) retrieving documents likely to contain stories from the online sources;

(b) extracting candidate stories from the documents; and

(c) filtering the candidate stories to identify stories with predefined levels of sentiment;

(d) preparing the filtered stories for spoken presentation by animated characters; and

(e) presenting the prepared stories using computer generated speech by the animated characters.