Summarizing Blog Entries versus News Texts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As more and more people are expressing their opinions on the web in the form of weblogs (or blogs), research on the blogosphere is gaining popularity. As the outcome of this research, different natural language tools such as querybased opinion summarizers have been developed to mine and organize opinions on a particular event or entity in blog entries. However, the variety of blog posts and the informal style and structure of blog entries pose many difficulties for these natural language tools. In this paper, we identify and categorize errors which typically occur in opinion summarization from blog entries and compare blog entry summaries with traditional news text summaries based on these error types to quantify the differences between these two genres of texts for the purpose of summarization. For evaluation, we used summaries from participating systems of the TAC 2008 opinion summarization track and updated summarization track. Our results show that some errors are much more frequent to blog entries (e.g. topic irrelevant information) compared to news texts; while other error types, such as content overlap, seem to be comparable. These findings can be used to prioritize these error types and give clear indications as to where we should put effort to improve blog summarization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it